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<body>
<pre><code class="r">##############################################################
##############################################################
### Replication Materials for
### Stefan Müller and Liam Kneafsey:
### Evidence for the Irrelevance of Irrelevant Events
### Political Science Research and Methods
###
### Please get in touch with the authors if you have any questions: 
### stefan.mueller@ucd.ie
### Note: 0000_readme.pdf contains 
### instructions and details about each script and dataset
##############################################################
##############################################################

## load packages
library(dplyr)        # CRAN v1.0.5
library(tidyr)        # CRAN v1.1.3
library(stringr)      # CRAN v1.4.0
library(ggplot2)      # CRAN v3.3.3
library(estimatr)     # CRAN v0.30.2
library(ggeffects)    # CRAN v1.0.1
library(modelsummary) # CRAN v0.8.0
library(here)         # CRAN v1.0.1

here::here()
</code></pre>

<pre><code>## [1] &quot;/Users/smueller/Dropbox/papers/ireland_incumbency_gaa&quot;
</code></pre>

<pre><code class="r">## alternatively, set working directory manually
# setwd(&quot;...&quot;)

## load code for default plotting scheme
source(&quot;function_theme_base.R&quot;)


## function to create a ggplot2 plot that plots the interaction 
## between match treatment (defeat, untreated, win) and incumbency status
plot_interaction &lt;- function(x, 
                             axis_title = &quot;Expected change in vote share\n(percentage points)&quot;) {

    data &lt;- x

    data$winner_loser_untreated &lt;- factor(data$x,
                                          levels = c(&quot;Defeat&quot;, 
                                                     &quot;Untreated&quot;, 
                                                     &quot;Win&quot;))


    data$group &lt;- factor(data$group,
                         levels = c(&quot;Incumbent (Candidate elected in t-1)&quot;, 
                                    &quot;Challenger (Candidate not elected in t-1)&quot;))

    ggplot(data, 
           aes(x = winner_loser_untreated,
               y = predicted,
               colour = winner_loser_untreated)) +
        geom_hline(yintercept = 0, colour = &quot;grey30&quot;, size = 1, 
                   linetype = &quot;dashed&quot;) +
        facet_grid(~group) +
        geom_linerange(aes(ymin = predicted - 1.96 * std.error,
                           ymax = predicted + 1.96 * std.error),
                       size = 0.5) +
        geom_linerange(aes(ymin = predicted - 1.645 * std.error,
                           ymax = predicted + 1.645 * std.error),
                       size = 1.3) +
        geom_point(size = 4) + 
        scale_y_continuous(limits = c(-3, 3),
                           breaks = c(seq(-3, 3, 1))) +
        scale_colour_manual(values = c(&quot;darkred&quot;, &quot;black&quot;, &quot;darkgreen&quot;)) +
        labs(x = NULL, y = axis_title) +
        theme(legend.position = &quot;none&quot;)

}


## Prepare data with local and general election results ----

## load local election results
dat_local &lt;- readRDS(&quot;data_elections_local_full.rds&quot;) 


## check how often each candidate-party-constituency observation
## is in the dataset (some candidates appear twice because their constituency
## was matched to two counties (e.g., Carlow-Kilkenny -&gt; team Carlow and Kilkenny)
dat_local &lt;- dat_local %&gt;% 
    group_by(const_link, party, date, candidate) %&gt;% 
    mutate(n_obs = n()) %&gt;% 
    select(n_obs, everything()) %&gt;% 
    arrange(-n_obs) %&gt;% 
    ungroup() %&gt;% 
    mutate(election_id = as.factor(election_id)) %&gt;% 
    mutate(party_recoded = as.character(party_recoded)) %&gt;% 
    mutate(county_gaa = as.character(county_gaa))



## load data for general elections
dat_general &lt;- readRDS(&quot;data_elections_general_full.rds&quot;)

## change coding of some variables
dat_general &lt;- dat_general %&gt;% 
    mutate(election_id = as.factor(election_id)) %&gt;% 
    mutate(party_recoded = as.character(party_recoded)) %&gt;% 
    mutate(county_gaa = as.character(county_gaa))


## check how often each candidate-party-constituency observation
## is in the dataset (some candidates appear twice because their constituency
## was matched to two counties (e.g., Carlow-Kilkenny -&gt; team Carlow and Kilkenny)
dat_general &lt;- dat_general %&gt;% 
    group_by(constituency_link, party, date, candidate) %&gt;% 
    mutate(n_obs = n()) %&gt;% 
    select(n_obs, everything()) %&gt;% 
    arrange(-n_obs) %&gt;% 
    ungroup()

## relevel factors for incumbency
dat_general$incumbency_status_party &lt;- relevel(factor(dat_general$incumbency_status_party), 
                                               ref = 2)


table(dat_general$incumbency_status_party)
</code></pre>

<pre><code>## 
## Party: Opposition 
##              3539 
## Party: Government 
##              2458
</code></pre>

<pre><code class="r">## get summary statistics on dependent variable
dat_local %&gt;% 
    summarise(mean_change = mean(diff_share),
              sd = sd(diff_share))
</code></pre>

<pre><code>## # A tibble: 1 x 2
##   mean_change    sd
##         &lt;dbl&gt; &lt;dbl&gt;
## 1     -0.0494  4.73
</code></pre>

<pre><code class="r">dat_general %&gt;% 
    ungroup() %&gt;% 
    summarise(mean_change = mean(diff_share),
              sd = sd(diff_share))
</code></pre>

<pre><code>## # A tibble: 1 x 2
##   mean_change    sd
##         &lt;dbl&gt; &lt;dbl&gt;
## 1       0.104  5.70
</code></pre>

<pre><code class="r">## relevel factors
dat_general$winner_loser_before &lt;- factor(dat_general$winner_loser_before,
                                          levels = c(&quot;Defeat&quot;, &quot;Untreated&quot;, &quot;Win&quot;))

dat_local$winner_loser_before &lt;- factor(dat_local$winner_loser_before,
                                        levels = c(&quot;Defeat&quot;, &quot;Untreated&quot;, &quot;Win&quot;))


## only select &quot;treated&quot; candidates
dat_general_treated &lt;- filter(dat_general, winner_loser_before != &quot;Untreated&quot;)


dat_local_treated &lt;- filter(dat_local, winner_loser_before != &quot;Untreated&quot;)

table(dat_general$difference,
      dat_general$winner_loser_before)
</code></pre>

<pre><code>##    
##     Defeat Untreated Win
##   2     19         0  45
##   3     96         0  84
##   4    160         0 216
##   5     83         0 135
</code></pre>

<pre><code class="r">## analyse number of matches that took place shortly before elections

length(unique(dat_general$match_id))
</code></pre>

<pre><code>## [1] 55
</code></pre>

<pre><code class="r">length(unique(dat_local$match_id))
</code></pre>

<pre><code>## [1] 54
</code></pre>

<pre><code class="r">##  Regression Models ----

lm_cand_share_general_base &lt;- estimatr::lm_robust(
    diff_share ~ 
        winner_loser_before * incumbent,
    clusters = county_gaa,
    se_type = &quot;stata&quot;,
    data = dat_general)


## for comparison, rerun model without clustered standard errors
lm_cand_share_general_noclusters &lt;- estimatr::lm_robust(
    diff_share ~ 
        winner_loser_before * incumbent,
    se_type = &quot;stata&quot;,
    data = dat_general)



## rerun model for local elections
lm_cand_share_local_base &lt;- update(
    lm_cand_share_general_base, 
    data = dat_local)


## run model for general elections with more fixed effects
lm_cand_share_general_all &lt;- estimatr::lm_robust(
    diff_share ~ 
        winner_loser_before * incumbent +
        party_recoded + election_id + 
        county_gaa,
    clusters = county_gaa,
    se_type = &quot;stata&quot;,
    data = dat_general)


## repeat for local elections
lm_cand_share_local_all &lt;- update(
    lm_cand_share_general_all,
    data = dat_local)


## only select treated candidates in general elections
lm_cand_share_general_treated &lt;- update(
    lm_cand_share_general_all,
    data = dat_general_treated)


## only select treated candidates in local elections
lm_cand_share_local_treated &lt;- update(
    lm_cand_share_general_all,
    data = dat_local_treated)


## Table 01 ----

models_main &lt;- list()
models_main[[&#39;M1: General elections&#39;]] &lt;- lm_cand_share_general_base
models_main[[&#39;M2: General elections&#39;]] &lt;- lm_cand_share_general_all
models_main[[&#39;M3: Local elections&#39;]] &lt;- lm_cand_share_local_base
models_main[[&#39;M4: Local elections&#39;]] &lt;- lm_cand_share_local_all

rename_coefs &lt;- c(&quot;Intercept&quot; = &quot;(Intercept)&quot;,
                  &quot;winner_loser_beforeUntreated&quot; = &quot;Untreated (ref. = Defeat)&quot;,
                  &quot;winner_loser_beforeWin&quot; = &quot;Win&quot;,
                  &quot;incumbentIncumbent (Candidate elected in t-1)&quot; = &quot;Candidate elected in t-1&quot;,
                  &quot;winner_loser_beforeUntreated:incumbentIncumbent (Candidate elected in t-1)&quot; = &quot;Untreated : Candidate elected in t-1&quot;,
                  &quot;winner_loser_beforeWin:incumbentIncumbent (Candidate elected in t-1)&quot; = &quot;Win : Candidate elected in t-1&quot;)


rows_add_4 &lt;- tribble(~term, ~m1, ~m2, ~m3, ~m4,
                      &#39;Election FE&#39;, &#39;&#39;, &#39;✓&#39;, &#39;&#39;, &#39;✓&#39;,
                      &#39;Party FE&#39;, &#39;&#39;, &#39;✓&#39;, &#39;&#39;, &#39;✓&#39;, 
                      &#39;County FE&#39;, &#39;&#39;, &#39;✓&#39;, &#39;&#39;, &#39;✓&#39;)
attr(rows_add_4, &#39;position&#39;) &lt;- c(11, 12, 13)

## print table in Viewer pane
modelsummary(models_main,
             add_rows = rows_add_4,
             statistic = &quot;conf.int&quot;,
             fmt = &quot;%.2f&quot;,
             stars = c(&#39;*&#39; = .05, &#39;**&#39; = .01, &#39;***&#39; = .001),
             coef_omit = &quot;(party_*)|(election_*)|(county_gaa*)|(Intercept*)&quot;,
             coef_rename = rename_coefs,
             gof_omit = &quot;se_type&quot;,
             align = &quot;lll&quot;)
</code></pre>

<table class="table" style="width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:left;"> M1: General elections </th>
   <th style="text-align:left;"> M2: General elections </th>
   <th style="text-align:left;"> M3: Local elections </th>
   <th style="text-align:left;"> M4: Local elections </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Untreated (ref. = Defeat) </td>
   <td style="text-align:left;"> 0.48 </td>
   <td style="text-align:left;"> 0.73 </td>
   <td style="text-align:left;"> -0.16 </td>
   <td style="text-align:left;"> -0.15 </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> [-0.43, 1.40] </td>
   <td style="text-align:left;"> [-0.16, 1.61] </td>
   <td style="text-align:left;"> [-0.88, 0.56] </td>
   <td style="text-align:left;"> [-0.86, 0.56] </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Win </td>
   <td style="text-align:left;"> 0.63 </td>
   <td style="text-align:left;"> 0.86 </td>
   <td style="text-align:left;"> -0.01 </td>
   <td style="text-align:left;"> -0.45 </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> [-0.41, 1.68] </td>
   <td style="text-align:left;"> [-0.21, 1.93] </td>
   <td style="text-align:left;"> [-0.87, 0.86] </td>
   <td style="text-align:left;"> [-1.26, 0.37] </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Candidate elected in t-1 </td>
   <td style="text-align:left;"> -0.50 </td>
   <td style="text-align:left;"> -0.93 </td>
   <td style="text-align:left;"> -1.07** </td>
   <td style="text-align:left;"> -1.44*** </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> [-1.61, 0.60] </td>
   <td style="text-align:left;"> [-2.21, 0.35] </td>
   <td style="text-align:left;"> [-1.69, -0.45] </td>
   <td style="text-align:left;"> [-2.10, -0.79] </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Untreated  ×  Candidate elected in t-1 </td>
   <td style="text-align:left;"> -1.65** </td>
   <td style="text-align:left;"> -1.54* </td>
   <td style="text-align:left;"> -0.06 </td>
   <td style="text-align:left;"> 0.05 </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> [-2.84, -0.45] </td>
   <td style="text-align:left;"> [-2.81, -0.26] </td>
   <td style="text-align:left;"> [-0.83, 0.70] </td>
   <td style="text-align:left;"> [-0.78, 0.88] </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Win  ×  Candidate elected in t-1 </td>
   <td style="text-align:left;"> -1.64* </td>
   <td style="text-align:left;"> -1.63* </td>
   <td style="text-align:left;"> -0.21 </td>
   <td style="text-align:left;"> 0.02 </td>
  </tr>
  <tr>
   <td style="text-align:left;box-shadow: 0px 1px">  </td>
   <td style="text-align:left;box-shadow: 0px 1px"> [-2.88, -0.40] </td>
   <td style="text-align:left;box-shadow: 0px 1px"> [-3.07, -0.19] </td>
   <td style="text-align:left;box-shadow: 0px 1px"> [-1.19, 0.77] </td>
   <td style="text-align:left;box-shadow: 0px 1px"> [-0.89, 0.93] </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Election FE </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Party FE </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> County FE </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Num.Obs. </td>
   <td style="text-align:left;"> 5997 </td>
   <td style="text-align:left;"> 5997 </td>
   <td style="text-align:left;"> 8674 </td>
   <td style="text-align:left;"> 8674 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> R2 </td>
   <td style="text-align:left;"> 0.031 </td>
   <td style="text-align:left;"> 0.100 </td>
   <td style="text-align:left;"> 0.012 </td>
   <td style="text-align:left;"> 0.087 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> R2 Adj. </td>
   <td style="text-align:left;"> 0.030 </td>
   <td style="text-align:left;"> 0.089 </td>
   <td style="text-align:left;"> 0.012 </td>
   <td style="text-align:left;"> 0.081 </td>
  </tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; border:0;" colspan="100%">
<sup></sup> * p &lt; 0.05, ** p &lt; 0.01, *** p &lt; 0.001</td>
</tr>
</tfoot>
</table>

<pre><code class="r">modelsummary(models_main,
             add_rows = rows_add_4,
             statistic = &quot;conf.int&quot;,
             fmt = &quot;%.2f&quot;,
             stars = c(&#39;*&#39; = .05, &#39;**&#39; = .01, &#39;***&#39; = .001),
             coef_omit = &quot;(party_*)|(election_*)|(county_gaa*)|(Intercept*)&quot;,
             coef_rename = rename_coefs,
             gof_omit = &quot;se_type&quot;,
             align = &quot;lll&quot;,
             output = &quot;tab_01.docx&quot;)
</code></pre>

<pre><code>## [1] &quot;tab_01.docx&quot;
</code></pre>

<pre><code class="r">## Figure 01 ----

str(dat_general$election_id)
</code></pre>

<pre><code>##  Factor w/ 31 levels &quot;1922&quot;,&quot;1923&quot;,..: 10 10 10 10 10 10 10 10 10 10 ...
</code></pre>

<pre><code class="r">dat_eff_share_general &lt;- ggpredict(lm_cand_share_general_base,
                                   terms = c(&quot;winner_loser_before&quot;,
                                             &quot;incumbent&quot;))

plot_interaction(dat_eff_share_general)
</code></pre>

<p><img 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68Gvowanm5ULXwcrF69OnCjY/57kmUo6HSdy7eSrYyH4avBLJNL+fAOk/5JOO28886BG6FI2DodhsD1hP29GxX0DQDXC048/+c//+nTLb4yagTF1XXZ8jf5+8slDW56Jbj00ksT8VHc/9//+3++s5Yen1z8y5ZviYCSLvRdJQv4OIZJr6VcVok5+IkTJ5rrfZvrMTtm/zVO+IaXOf1Ko9F9RAm3LVu2NCeME/caypWRMoqGoWScIPC/+qew5d4JbHOF0pzQs/r163vlnNCRlHM0rOxyyFu5nkFKmJoKkLZurkZTBGFcwne23377DYa9w2fpvxpaV3w1vOaEkr92FaAfag7dxsXRffh+iDtZUURTFmIZZVwjyfbZZ5+UR9L019CbjKYpNJWiYUgNy2vaRfOeMsnDfd7C/cuFc3pcFGcZV1GmTCe4XpSJZ7qRe00VaNguNGFc5F7+i1FyuqRpPXjw4NB5ym82/xYsWFAipime/+8mLs+i3CfbafojeW5Qw8yuUZLsJOXaNWxT7tNvcskjvRMXZ019qSzpWwmNpndCbfKShKFplExGw9kqt8llylXc3nmmZajJfrnGsZ8+kEa3yrmG5l3Dz3+nye40LB8alXkZTYstW7bMXAci5VvSd7n33nuHzlN+cyl/uZTxZE9LWj7Cd8U1uaxpeiGsy1RONBUQ6gnpHdWdynPVP8n1bOhf8m+u+Ru+ky0NmiqRGyfgw1d83DMp72bzr6T5lgg07SKOYZrTxO2vEi9hVfkuJHTdcEtswpSpyUYfTbJRZVG3bt2ElYSw5tjiTCj0QzdaoqJ3VFgVnmuRpXy80syVUUUio0ZBcqMkuYB4B1n+qXINK7nQqebnHn/88fA25VfzdVLAk4b9wQcf7IWp5uC7dOniK083DGWapw8bIHo5Lo5iKGGnP1VEMrpWwY0yeqbCkGxcryZxq+Va0nWQHoLm61ShK77Sb4gyuXBO56NKR/FTXiQ/072bktkgGLmXTkOYd6GDQ9zcnSp/CR83hZAxzaH78Debf9JBKAnT0N/k37g8S3YXda34JeuSqKFbFpNLHsn/uDjru1AjKzm/pEcSCoxcw1DZDN+JSpO+RZXb5HKoeXIp1IX6HunvaX7+hRde8I0i6Vbou23t5phVpqT8qIafGxlKeS25XgnDUplTvus3vYyk112hZ7mUPzWYs5Xx0D/9lrR8hO8m152yC9Ola+WPdHSSjeoLlZuw3kh+ln6da/6G72VLgzjLTfL3FL4b9ZuLfyXJt6gwZBfHMNM7VULAqwepJSzJxg1NeWUWN/znha6Eb7JRSzlZkSb5Wa7XGjlQT0JGlZAUyLTWXJWCej7KdClchcYNMXrlq1w/LL0nPzIZCXM3VJnyWC1jKRg6PYKUpXNypPXyUqpSHFVopIEvt25oKuGHBKqUz3IxanmH6ZZglnFDmZYstJP9UXylNKgKK2wEJOebKkv12LUxj5SpZNwwpP9N9jNkUhrOynOFr9/k5XviFfamfID/+6dvyw1f+oZGWDFLudHpTfj1+xoxUUNLlbt67jJSsJPiYzgyEcZXz7L5V1Km8rM8TBhHxc/pnOTsZSbhE3pQmjwK3w1/pYwk5voutt12W28tgRsKwvIIQ55K+Kt3rZGjZOPmik37SqhsKL9Do3yXcpTYqbGvkQ8pxzn9kYSAU1lL/nbDd6N+QyVOjWBp6aOMwnjrrbci95LI9q3kUsaTBbHCK2n50DvZjPx001op5d5N4fm0iVdowm8wvA9/S5q/2dKgcqz6Rd+QFC9Do6WQTufBKw3LLoxPNv9Kmm9heOXxG92VKg+fC8gPadcqs6699lrTEKdTGPPCLOz5qSetQiNhIc1RtWrd3GmZU6BhWFXsWouutalqkYZDuRLs2qTGzTV5jWuF7eZKfUWVa8AaldDwlob2o4wEpoYUww9RbpRmrQ5Q+KpwxEJha82lU36xs846y48qqOWsVrUqM1UiaqVqaZ0aPtIYzcVIq1jDhwMHDvRD/MoDN+eY8VX1aBSOm4P0m4i4+aeUzYLU81FawqFyDRdrBYRMGCcxEW81rlSBlZSzNJxVqbi5UnNKfH741M1B++E6TRGkG62mUCNGWteKj3Yx07CnRkmkfasemiparcrQFIS+P32HGlJWzy89vtn8KynT9PiW5j49jiXxQ99ONlPSPEr3T71ifesSetrQSY1arZhJNmUNI/RL4Wi6KtncfvvtiQahpo7UgNMGJRIOKi8a2pWgVMNY38jChQv9dyyNeqdQluiZJ/uZ6Vr1h1bWyH+NTKqspHdOwnezfSu5lPGw16j9NpyCmV8BUJLyEcYl7lcjhiqzauwojA8++MB3LsQ6bBRnq+tKkr+5lHGVQ9WHaoRrJMTpaHjmWvUgkxyfXPwrSb7FsSrxM1dhVgkjZTHXAwtc7zhwQ6peE9ZlnE+7NIWl2ezgec12tzmF16p3LXL/XEo+rjCkcHI984SymR5Ii1vvS0nHVWr+2lXkgavIfZhShJEGfmgUthOqXktcfktzVgplelcmKkwnNFLiIcUR19r0f64XEHqd+JXGvuLkGhkJO124XpVX1JEiiXjIjeIpBTY3PJVw6z7uwBUw/8x90H5VgRRdXC/Fu8kljm5IOXAFwIejsNyGOj6+UUp28lTaq1IEdEOyXvlIqwtCJTs9dwLdK7zJTprtrucUuIoscGv39Thww/demVFpkl/ZOPuX0v7JDzeE6jWelTfSrJZSZmiSlexk54RK4IaqfZzFSc9dIyR07pUC3XSC90+a2m75Y0J5Mj2+ufiXjWki4P9dpCvZpX/L6d9V+vtRcUx3k+n+7LPP9o+SlZxcg8h/c6EWfbY8yuU7k3az0qnvQt+nVmzoG1DeyGQLQ0p2yrtsRhrgTsBt4EzKlK4j4cNWuPpT2XHD8wm3bp4+cCNZvsy7XmcghVUpxcmt6g2V4eQ468XQTt9yaKT8JU16lREpn0nbPEqLXu6zfSvZyrj8cKNN/tsNV4ZkKx96J9lIyU6rjJKNtM5D/2Svb1D1stLkRkr86hvXGE68kq2uy5a/yd+fPM2WBvnnluol6i3VAVL8C016fLL5p/fi8i30N/lX30Kykl02hsnvhtdqSVYpo6UeWv4RZSTo3faTUY9KbSeBLU1vCdUok+151DvJdhLIWqqTyahwarlZJuOGMQNVPFENhPAdMdMHXxajwqqwcjWqMDPFSWlWnOKM0ydIYV4azuIaLk+KCyt85npnfglYeJ/+qzjpL8qkx1dusvlXUqZR4ZbELiqOJXk/m9vS5JH8FActUUwu12KnCtL1BlOCLW0YoSdqSEg4f/TRR6HVBr8SqlrumMm4HnzG7yDTO+n2KgNa/puryfatZCvjKgvJjX+FW9LykUtcFQ838hbpNFtdp5dKmr/Z0qB6T99SlImKTzb/SppvUeGWxK6aHLuCgKmkBDREKO1dTRVorgoDgcpGQPPvmjPVVJA2YnINEa83ohUfmmIqiU5LLmw0ZaN59FzOgcjFP9xAIF8EqsQcfL7gFYO/mufVnLC2oMVAoDIS0JIrKWdKZ0MKTVq9IKEvpdXyFu7iJ70PzcOnK9tVRrakqbgJ0IMv7vzLKfZSQHPTDwkN45xewhEEipCAeu9SgIpb7lYeyZIymBoPjIqVB038yBcBBHy+yOIvBCAAAQhAoAIJMERfgfAJGgIQgAAEIJAvApV+oxu3h7NfGxtunJIvkPgLgapMQHPf2uUw3WiNuNOMTrfmHgIQKEcCWoufvveDvK/0Al7KMP3ceczaThQDAQjkh0C44VC67zruV5u5YCAAgfwQcMuJvYyLEvAM0eeHOb5CAAIQgAAEKpQAAr5C8RM4BCAAAQhAID8EEPD54YqvEIAABCAAgQolgICvUPwEDgEIQAACEMgPAQR8frjiKwQgAAEIQKBCCSDgKxQ/gUMAAhCAAATyQwABnx+u+AoBCEAAAhCoUAII+ArFT+AQgAAEIACB/BBAwOeHK75CAAIQgAAEKpQAAr5C8RM4BCAAAQhAID8EEPD54YqvEIAABCAAgQolgICvUPwEDgEIQAACEMgPAQR8frjiKwQgAAEIQKBCCSDgKxQ/gUMAAhCAAATyQ6DgBXwQBPbee+/Zp59+auvXr88PBXyFAAQgAAEIVDICBX0e/OTJk+3YY4+1du3amc6Vbt26tT377LNWt27dSpYNJAcCEIAABCBQvgQKugfft29f69Onj40ZM8amTJli33zzjT311FPlSwDfIAABCEAAApWQQEH34NVbr127tse+Zs0aW7ZsmW2ySUG3SSrhJ0KSIAABCECgGAkUtIBv0KCBZzp48GAbOnSo7b///nbyySencD7vvPNs5cqVKXbJN2oQ9OrVK9mKawhAAAIQgEClJ1DQAj6kr158x44d7d1337UPPvjAunTpEj7ygj9xE3ExaNCgCFusIAABCEAAApWbQFGMd59zzjk2fPhw69mzp/Xv379y5wipgwAEIAABCJQDgYIV8GvXrrXLL7/cfvjhh0Qy27dvb9OmTUvccwEBCEAAAhCAQDSBghXwNWrUsNmzZ9vAgQNt3bp1Nn/+fNNcfLdu3aJTgi0EIAABCEAAAgkCBSvgFcMbb7zR5syZY23btrVtttnGmjZtanfddVci8lxAAAIQgAAEIBBNoKCV7Lbbbju/sc3ixYutWrVqFmrVRycFWwhAAAIQgAAEQgIFLeDDSG6++ebhJb8QgAAEIAABCORAoKCH6HOIP04gAAEIQAACEIgggICPgIIVBCAAAQhAoNgJIOCLPQeJPwQgAAEIQCCCAAI+AgpWEIAABCAAgWIngIAv9hwk/hCAAAQgAIEIAgj4CChYQQACEIAABIqdAAK+2HOQ+EMAAhCAAAQiCCDgI6BgBQEIQAACECh2Agj4Ys9B4g8BCEAAAhCIIICAj4CCFQQgAAEIQKDYCSDgiz0HiT8EIAABCEAgggACPgIKVhCAAAQgAIFiJ4CAL/YcJP4QgAAEIACBCAII+AgoWEEAAhCAAASKnQACvthzkPhDAAIQgAAEIggg4COgYAUBCEAAAhAodgII+GLPQeIPAQhAAAIQiCCAgI+AghUEIAABCECg2Akg4Is9B4k/BCAAAQhAIIIAAj4CClYQgAAEIACBYieAgC/2HCT+EIAABCAAgQgCCPgIKFhBAAIQgAAEip0AAr7Yc5D4QwACEIAABCIIIOAjoGAFAQhAAAIQKHYCCPhiz0HiDwEIQAACEIgggICPgIIVBCAAAQhAoNgJIOCLPQeJPwQgAAEIQCCCAAI+AgpWEIAABCAAgWIngIAv9hwk/hCAAAQgAIEIAgj4CChYQQACEIAABIqdAAK+2HOQ+EMAAhCAQN4IDBn1d1u8fHHe/M+nxwj4fNLFbwhAAAIQKGoCA5+43hYuW1CUaUDAF2W2EWkIQAACEIBAPAEEfDwfnkIAAhCAAASKkgACviizjUhDAAIQgAAE4gkg4OP58BQCEIAABCBQlAQQ8EWZbUQaAhCAAAQgEE8AAR/Ph6cQgAAEIACBoiSAgC/KbCPSEIAABCAAgXgCCPh4PjyFAAQgAIEqSuClj1+w1b+stmc/eLooCSDgizLbiDQEIAABCOSbwO0jb7KVa1Za/0evyXdQefEfAZ8XrHgKAQhAAAIQqFgCCPiK5U/oEIAABCAAgbwQQMDnBSueQgACEIAABCqWAAK+YvkTOgQgAAEIQCAvBBDwecGKpxCAAAQgAIGKJYCAr1j+hA4BCEAAAhDICwEEfF6w4ikEIAABCECgYgkg4CuWP6FDAAIQgAAE8kIAAZ8XrHgKAQhAAAIQqFgCCPiK5U/oEIAABCAAgbwQQMDnBSueQgACEIAABCqWAAK+YvkTOgQgAAEIQCAvBBDwecGKpxCAAAQgAIGKJYCAr1j+hA4BCEAAAhDIC4GiEPATJkywqVOn5gUAnkIAAhCAAAQqI4EahZyoTz75xHr16mUtWrSwFStWWBAE9vTTT1vbtm0LOdrEDQIQgAAEIFDhBAq6B3/DDTfY+eefb++8846NGzfO9t13X7vzzjsrHBoRgAAEIAABCBQ6gYLuwV9wwQXWuXPnBMM2bdrYyJEjE/dcQAACEIAABCAQTaCgBfwxxxyTiPXy5ctt6NChvkefsHQXstfQfSazdu3aTI+whwAEIAABCFRaAgUt4EPqEuInnniitW7d2vr27Rta+9/rrrvOVq1alWKXfLN69erkW64hAAEIQAACVYJAwQv4BQsWWLdu3ax58+b25JNPWo0aqVG+6667YjNq0KBBsc95CAEIQAACEKiMBApaye6nn36yQw45xNq1a+fn3uvUqVMZ84A0QQACEIAABMqdQGp3uNy9L5uHZ511lh+Wv/XWW23evHnes+rVq1vTpk3L5jFvQwACEIAABCo5gYIV8JMnT7bRo0d7/C1btkxkQ6tWrWzGjBmJey4gAAEIQAACENiQQMEK+I4dO8Zqx2+YFGwgAAEIQAACEAgJFPQcfBhJfiEAAQhAAAIQKBkBBHzJeOEaAhCAAAQgUBQEEPBFkU1EEgIQgAAEIFAyAgj4kvHCNQQgAAEIQKAoCCDgiyKbiCQEIAABCECgZAQQ8CXjhWsIQAACEIBAURBAwBdFNhFJCEAAAhCAQMkIIOBLxgvXEIBAJSLww/jx9i7nVVSiHCUpyQQQ8Mk0uIYABKoUgQXTp9uUF/9dpdJMYqsOAQR81clrUgoBCEAAAlWIAAK+CmU2SYUABCAAgapDAAFfdfKalEIAAhCIJbBw4UIbMGBArBseFg8BBHzx5BUxhQAEIJBXAj///LPdfffdeQ0DzzceAQT8xmNNSBCAAAQgAIGNRgABv9FQExAEIACBwibw9NNP2+rVq+3FF18s7IgSu5wIIOBzwoQjCEAAApWfwNVXX20rVqywgQMHVv7E5pDC9evXe1dBEOTguvCcIOALL0+IEQQgsBEILPnxRxv3zxE2/8sv7dORIzdCiARRLAR+WPC9Xf7gxTZl1uc+yoEF9vs7e7n7L4olCT6eCPiiyi4iCwEIlBeBZXPn2tR/v2Q/z5xlX7/+Rnl5iz9FTmDh0oXW46ZuNnTMYFv1y6pEakZ+8KTtfVkHG/f1xwm7Qr9AwBd6DhE/CEAAAhDYKATW/LLGTnDCXT339cF/h+fTA+4z5Dybs2h2unVB3iPgCzJbiBQEIAABCGxsApNnTLTZi36MDfbr2V/aG5Nfi3VTKA8R8IWSE8QDAhCAAAQqlMCs+TPtp5/nxMZh1ZpVNuGbcbFuCuUhAr5QcoJ4QAACEIBAhRKQMt0m1bKLxWpWrULjmWvg2VOSq0+4gwAEIAABCBQxgbbNtrPmjVrEpmCz2pvZb7bfJ9ZNoTxEwBdATqx020NiIAABCECgYgl0aNXRdmuzh1WrlrmHXqtmbTt0t8MrNqI5ho6AzxFUvpytWrLE+jdqbOGGCvkKB38hAAEIQCCeQI3qNezeCx+whps2tJrVa27guEWjrWzsLR9ZkwZNNnhWiBYI+ELMFeIEAQhAAAIVQqBx/cY2ZfB39n8nXWd1a22aiMO5R1xoYwaMtbbNt0vYFfoFAr7Qc4j4QQACEMgzga+//tr69OmTGEn87LPP/Ha1y5Yty3PIhel9vbr17Oqef7Zdtm3vIyjFu7vPu7eohLsijoAvzO+LWEEAAhDYKASmT59uBx98sA0ePDgRnvajv/HGG61Vq1a2YMGChD0XxUUAAV9c+UVsIQABCJQbgfnz59s+++xjP7p9+dMPVFmzZo0tWrTI/vCHP9jatWvLLUw82ngEEPAbjzUhQQACECgoAq+++qo/HjZTpCT0J0yYYJMnT87kBPsCJoCAL+DMIWoQgAAE8kngo48+suXLl8cGMXv2bNMcPab4CCDgiy/PiDEEIACBjUZAvfi4deEbLSIEVGICCPgSI+MFCEAAApWDwJ577ml169aNTUzz5s29sl2sIx4WJAEEfEFmC5GCAAQgkH8Chx9+uO24446xAbVo0cJ23333WDc8LEwCCPjCzBdiBQEIQCDvBCS8hw4dGhnOJptsYu3bt7cxY8ZYrVq1It1gWdgEEPAVnD8/jBvvY/DD+P/+VnB0CB4CEKhiBPbee2/7/PPP7ZRTTkmkvE6dOn7jm9GjR1ujRo0S9lwUFwEEfAXn1yOnnOpj8Oipv63gmBA8BCBQVQnssssu9sQTTySU6Tp06GCDBg2yrbfeuqoiqRTpRsBXimwkERCAAATKTiDUltfwPKb4CZCLxZ+HpAACEIAABCCwAQEE/AZIsIAABCAAAQgUPwEEfPHnISmAAAQgAAEIbECgxgY2ERYvv/yyjRo1ynbddVfbbbfdrEmTJrbddsVzJm5EkrCCAAQgAAEIVGoCWXvw11xzjZ177rn+sIGJEyfazJkzbY899rD333+/UoMhcRCAAAQgAIFiJhDbg//555/tjjvusGnTptmHH35o77zzjvXs2dMfIfjoo49ap06dijntxB0CEIAABCBQaQnE9uBnzZplO+20k7Vp0yYFgNZMTpo0KcWOGwhAAAIQgAAECodArIDfYYcdbMaMGfb6668nYrxixQq75557rGPHjgk7LiAAAQhAAAIQKCwCsUP02q5wyJAh1r17d2vYsKHVrFnTRowYYU2bNmUOvrDykdhAAAIQgAAEUgjECni5lFCXct3bb79t8+bN89rzPXr0MAl/DAQgAAEIVB4CV155pd+i9qKLLqo8iarCKYkV8NOnT7devXrZ4sWL7fzzz6/CmEg6BCAAgcpPQPX8/fffb2eccUblT2wVSGHsHPw222xjnTt3tuHDh9uCBQuqAA6SCAEIQAACEKgcBGIFvBTqpGR3ySWX+M1tatSoYeGfevYYCEAAAhCoPAQaNGhA773yZKfFDtErs1977bXI5NavXz/SHksIQAACEChOAtqlVKukMJWDQKyA15GBWgePgQAEIAABCECguAjECngl5ZdffrF3333XK9rpft26daah+/Xr19uZZ54pKwwEIAABCEAAAgVGIKuAP+yww+w///mP1a1b1zbddFPT9rVr1671SykKLC1EBwIQgAAEIACB/xGIFfDff/+9ffLJJ/bjjz/a4MGDrV69etanTx87/fTTrUWLFkCEAAQgAAEIQKBACcRq0S9cuNAfEbvFFlv4E+Tee+89r0V/9dVX27Bhwwo0SUQLAhCAAAQgAIFYAa8z33WSnHry2ntec/Gak1+6dKmtWrVqo9LTSXbacAcDAQhAAAIQgEB2ArECfrPNNvNr4Pfcc0/beuutrV27dqbr448/3rRd7cYyWqrXpUsXfxb9xgqTcCAAgcpLIAgCW/TdzEQCl86ZY7+sXJm45wIClYFArIBXAgcMGODPgtf1c88957esffLJJ613796yyqtZvny5XXHFFX7jBRVIDAQgAIGyEli7erU91+cie/6iixNefTl6jN27Xydb6Db2wkCgshDIKuA1JK+5+Keeespeeuklv6OdDp3RfHy+zYQJE/wBN+PHj7datWrlOzj8hwAEKjmB9W6Z7z+6uJVBDzxoi93UY2h+cUt/Z0+ebP84uIvN/vTT0JpfCBQ1gVgteqXshBNOsNGjR1v79u1ThGzXrl39PvX5TP0BBxxg+oszDz30kK1Zsyajk2+//TbjMx5AAAJVi8Bnzz5rP33+ua13HZco8/PMmTbq6mvsnH+/GPUYOwgUFYFYAT9r1ix74403TL+Fuixu55139uvyM1HXEj8MBCAAARGY9PgTtnrJklgY86ZOswXffGON27aNdcdDCBQ6gVgBr3lvnShXqMJdcPfbb79Yxhrex0AAAhAQgZ9dZyWbWbtqpS2bOxcBnw0UzwueQOwc/LbbbuuH5v/2t7/5efiCTw0RhAAEIBBDoG7DhjFP//uoeq3aVttt6oWBQLETiBTwZ5xxhlWrVs3/PevmrHRcbOPGjRN2enbKKacUe9qJPwQgUMUItOve3WrWrROb6npbbmlbcshWLCMeFgeByCH6O+64w/r16xebAm1bi4EABCBQTAT2OP00G//ww/bD+AkZo931hn5WvWbNjM95AIFiIRDZg2/WrJltv/32/k+7xz3//PP++uWXX7ZOnTrZRRdd5A+e2ZiJXOk2odh11103ZpCEBQEIVDICm7mRyHNffcU2bbyF1XIbeSWMG5Vs7Oq8nsMetHZHHZWw5gICxUwgUsCHCdKpcccdd5xfhiZBr01nbrjhBj9c379//9AZvxCAAASKhsCm7myNq779xrr2/3WUslXnTnbOqJfsN2efVTTpIKL5J3DL7/9qm9XezAadPyT/geUhhFgBP336dL+xjQ6XUe9d29VeeOGF1rdvX3/KXB7ig5cQgAAE8k6gdv36tkPXwxLhtHBnbTRxPXgMBJIJdNr5AKtZo6YdvOuhydZFcx0r4DfZZBNbtmyZT4y2qdWmNzJffvml7YQSimfBPwhAAAIQgEAhEohUsgsjusMOO5jm43fbbTf7+uuv/Z70zzzzjO/FP/7446EzfiEAAQhAAAIQKDACsQJecX3llVds1KhR1qFDB9OucdoW9qOPPvInyxVYWogOBCAAAQhAAAL/I5BVwGs5XM+ePRPA9tprr8Q1FxCAAAQgAAEIFCaBrAK+NNHWCXQ6UnbcuHH2uTvYYf78+X6YX2fJa5mdfjEQgAAEIAABCOSPQKySXWmCHTlypBfmw4cPt7p163rFvMsuu8y07a2OmNVowPHHH29fffVVabznHQhAAAIQgAAEciBQrj34P/zhD/5gmrffftsvr4sKf/369abtb88++2y75ppr7Oijj45yhh0EIAABCEAAAmUgkJOA1xp4KdppJzlp1Ddp0sS22267DYLVoTSbbrrpBvbJFlp6d9JJJ/m/cAle8nOuIQABCEAAAoVC4O7zBtuWDZoWSnRKFI+sQ/TqZZ977rk2efJkmzhxos2cOdP22GMPe//99zcIKJtwT3+B/ezTiXAPAQhAAAKFRODkzqdavbrFefZKrID/+eefTQfPaO68d+/enrnm0GX36KOPxuaBFOz++te/ejeDBg2ypk2b2lFuj+clS5bEvsdDCEAAAhCAAATKTiBWwM+aNcvvWNemTZuUkHbZZRebNGlSil3yDXvYJ9PgGgIQgAAEILDxCcQKeO1kN2PGDHv99dcTMVuxYoXdc8891tHt3ZzJsId9JjLYQwACEIAABDYOgVgluzp16tiQIUOse/fu1rBhQ6vpzkgeMWKEH26PmoMPo8we9iEJfiEAAQhAAAIVQyBWwCtKp59+ut+c5tVXX7V58+Z57fkePXqYhH8mwx72mchgDwEIQAACENg4BGIFvPad1/B8t27d7Pzzz0/E6Ntvv/Ua9eHpcokH/7vQezo7ft26dSl72OugmqVLl6Y75x4CEIAABCAAgXImECngFyxY4OfepUV/6aWX+iH5MNwgCPw2tF988UXi+NjwWfJ7V111lT322GOmOXtp1Os9HVwT9V74Pr8QgAAEIAABCJQPgUgBL2F84okn2uLFi/158F27dk2Epvl1bXRz0003JezCi9K+F77PLwQgAAEIQAAC5UMgUsBLgH/33Xd+zfoll1xi2lc+F1Pa93LxGzcQgAAEIAABCOROIFLAh683aNDAC3cNtWu72uXLl/utak899dTY8+DD90J/+IUABCAAAQhAYOMSiBXwisrll19uw4YN87vQtW/f3u9Jf++993rlO+1Nn8noUJl3333Xa97rOjRbb7217b///uEtvxCAAAQgAAEI5IFArICXkp22mZ0yZYptv/32Pvgbb7zR/vSnP9ngwYPtvvvuyxglbUurtfKtW7e2WrVqJdwdfvjhCPgEDS4gkJ3AuYPOsE47H2jnHP7rSpbsb+ECAhCo6gRiBfzcuXNNa9pD4R7COvbYY01a8pnMN998Yx9//LHpV3vQYyAAgdITWLt+ra1bv670HvAmBCBQJQnEblW74447+uVtb775ZgKO9pnX7nYars9kateubZqH33LLLTM5wd4RWOi2Af5l1SrPYs2yZfbz99/DBQIQgAAEIFAuBGIFvELQcPwRRxxhOmBGO9hJU37s2LF2/fXXZ4xAy5Ytbd999/VD+Fpqh9mQwBs332IPHHGkSbDLLHO7BN7X6QAb+78T+DZ8AxsIQAACEIBA7gSyCvizzjrLb1Sjnewk5O+8806bOnWqSYhnMpq7f+edd+yiiy7ye9hXq1bNwr9TTjkl02tVxn70tdfZ6wNutAVfff1rmt3eA4vd6X1jrr7W3rnr7l/tuYIABCAAAQiUgkDsHLy2nP3eDRvr5Li40+PSw9XwvAR8lKlXr16UdZWxm/fll/bJ8Idt7erVkWle98sv9uGQf1iHk060RttuG+kGSwhAAAIQgEA2ArECfuXKlX6uXcPtZ599tp188sm22Wabxfqp3ey02912220X666qPvzGTW8sm/tTbPJ/njnTZrz7njU6DQEfC4qHEIAABCCQkUDsEP3mm29us2fPttNOO80eeOABa9Gihf3hD3/w69ujfDzjjDOsZ8+etnDhQi/kJejT/7RJTlU2i2Z8Z8HaeI3otU7xbjEKd1X5MyHtEIAABMpMILYHL991Drzm3/WnU+Qeeugh01p2HSMroZ9s/uoUxNSD1zvTp09PfpS4zjYCkHBYSS8abLWVVXMNnyBp85/0pFavVdPqt2iebs09BCAAAQhAIGcCWQW8fNLSuDFjxtgjjzxiL730knXp0sV++9vfbhBI8pr3tm3b+tPk0re4lX1VNm0OOtAattrWFn07IyOGes2aWav99sv4nAcQgAAEIACBbARih+hXO0Wwiy++2LZyvc6+ffuatqbVca8S2ocddlis39ri9o9//KNvHIRb3B500EH26aefxr5X2R+2cAzbu+WG1ZN290tO8yY1a9reTt+hidtgCAMBCEAAAhAoLYHYHvwqNxes89xHjhxpBxxwQM5hlGWL25wDKWKH3e/4i61YsMC+evU1W+p0HELTqE0b263Xqda1f7/Qil8IQAACEIBAqQjECngp2T344IMl9ri0W9yWOKAifUGKh6c+PNx+nDjRBnc+0Na4RtSmjRvbWS8+b81jdggs0uQS7TIQGDLq7zZm/Ch75/OxtnvbPW3vHfYtg2+8CgEIVCUCsUP0pQVR2i1uSxtesb631e67W81NN/XRr+MaUwj3Ys3J/MV73uK5tnTlEvvp5zm2bNV/dz3MX2j4DAEIVCYCsT34siQ03OJWh9VI4L/11ltWv359+/DDD8viLe9CAAIQgAAEIJADgawCvrTnumuL2z333NPeeOMN05D9cccdZ1oDX9WXyeWQJziBAAQgAAEIlJlAVgFflnPdtW5ef/Pnz7dGjRqZDp5BwJc5z/AAAhCAAAQgkJVArIAvy7nul112mT388MPWtWtX29btqf7444/boEGDbPTo0bFHzWaNMQ4gAAEIQAACEMhKIFbAl/Zc9yVLltiwYcNs2rRp1sxt2hKa6667zu655x67//77Qyt+IQABCEAAAhDIA4FYLfrSnus+z51tvs0226QId8X90EMPzbiFbR7ShpcQgAAEIACBKksgVsCX9lx3nSSng2n69etnM2bMMCnqffzxx9a/f39/Kl2VpU3CIQCBgiGgJapbtG1jtd3qnkbbblMw8SIiECgvArFD9KU9133RokU2fvx4e/XVV23AgAG2qStI2hFPRufE69Q5mTfffNMOOeQQf80/CEAAAhuTwJZu+W63W2+xj+4faof86U8bM2jCgsBGIZBRwJflXHftgJfLendNAWAgAAEIQAACECh/ApECXj3slStXemW4xm4L1Sijc9+ffPLJqEf+DPjtt98+8hmWEIAABCAAAQjkn0CkgOdc9/yDJwQIQAACEIBAPglECvj0c93zGQH8hgAEIAABCECg/AnEatGXf3D4CAEIQAACEIDAxiAQ2YMvr4BffvllGzVqlO2666622267WZMmTUxL6DAQgAAEIAABCOSXQN568Ndcc42de+65NnnyZJvozj2fOXOm7bHHHvb+++/nN0X4DgEIQAACEICA5dSDL2lPXBvk3HHHHX6rWi2X09p3ad1rffyjjz5qnTp1Aj0EIACBCidQu0EDa9SqVYXHgwhAIB8EsvbgS9MTnzVrlu20007Wpk2blDjvsssuNmnSpBQ7biAAAQhUFIGdjjzSTn5gaEUFT7gQyCuB2B58aXviO+ywg9+i9vXXX09EXjvZ6aCZjh07Juy4gAAEIAABCEAgPwRiBXxyTzx5Zzr1xEeMGJExRnXq1LEhQ4ZY9+7drWHDhlazZk3vXsvvmIPPiI0HEIAABCAAgXIjECvgy9ITP/300/1cu/aj1+ly0p7v0aOHSfhjIAABCEAAAhDIL4FYAV/anviaNWsS29jqoJlWToll7dq19swzz9hmm21maji0b98+vynDdwhAAAIQgEAVJhAr4MWlND3x1atXm7a71fK4Zs2a2dZbb20TJkywGjVqWNu2bf2SuYsvvthuvfXWnNDrqFkZLbOTHxgIQAACEIAABOIJxErL0vbE1WtftmyZjRw50g/LV6tWzRYsWGDHHnusSStfgnr//fe38847L3bjG72z3377mY6t/emnn3zP/9///rcfBYhPFk8hAAEIQAACVZtArIAvbU/8yy+/9D3tE044IUFXp9KdffbZ9vzzz9sxxxxjXbt2tXHjxsUK+L59+9qRbhnL3//+d1u1apUdffTRdt9999mVV16Z8JcLCEAAAhCAAAQ2JBAr4EvbE9cw/Pfff++H6HfffXcf6rp16/wcvDa50VnzU6dONQnwOPPGG2/4UQC5kT6ApgseeuihEgl4jSQ88sgjfhQgU1innXaa1a1bN9Nj++qrr+ztt9/O+FwPTjzxRGvUqFFGN9rJTwqH6UYNF5mlS5f4UQpNaWQyGsXQCEacOfzww23bbbfN6ERLH6ULEWcOPPBA23HHHTM60VHC2rAozuyzzz5+i+JMbvQNDBs2LNNjb6/tjX/zm9/EuvnnP/9pv/zyS0Y37dq1s86dO2d8rgdPPPGEH3HK5Ej7ORx66KGZHnt7NVznz5+f0c1WW21l3bp1y/hcD0aPHm0//PBDipuJX01I3L/33nvWZdfDEvdRF2+++aZ98803UY+8nXRgevXqlfG5Higclc9MRtNkZ555ZqbHKfZz5861Bx98MMUu+WaLLbaw5I5A8rPwujzSpNU7U6ZMCb3c4DeXNI0fP95PNW7wcpLFWWedZdWrV0+ySb38/PPPLXlFUurT/9799re/NdW9mczXX39tY8eOzfTY25e2Pkr2VKugmjdvnmyVcq28ffHFF1Ps0m/UkZMOViazePFie/rppzM99vYHHHCA31clkyPVoarj48zee++ddYl23Hcqv3Opj7S6TCPfmYz2h1F64oyOYV+6dGlGJ61bt7bDDouvB8KXYwV8aXvitWvXtltuucVUyUvAq3J76623fEZr7v3CCy/0hUB71GcygvTjjz/6d0M3LVq0sDlz5oS3/lfD/BI4mYxA6Z1atWplcuIrmDgBrwI1fPjwjO/rgYDHCXgtOYzyY2/3cdZ07yueEuBxAl4FKsqP5IjpA4oT8CpQ2fwQ5zgBrwKVzQ8Jkrj8Xb9+fVY/zjjjjKwCXgVbeyxkMscff3xWAa8KJv27SvavS5cuWQW8Krpp06Ylv5Zyvddee2UV8GPGjLFPPvkk5b3Z9b4zq/dfqw+0zXPvlMcb3KicqWGcyWipajYB/9FHH9mzzz6byQvfGM5VwKvRE/etaHVNNgGvxvVrr72WMT65pElCNS5N6kBkS5P0iOLSogjqm40T8F988UVWP/TNZhPw2eKhbzauPlJDMpsf+mbjBLxWR2XzQwrV2QR8Nj9UJ6pey2Q00pzND9XvcXuwqMORzQ91MLN1ONTxUacyk9E0dTYBrw6YZF8mc/DBB5ePgC9LT/yiiy6ygw46yAv2JUuWWP/+/b2wV6T/9Kc/bbDLXXpiNP8u6PXq/a92cw7CEYVkt0OHxu9CNXDgQFu+fHmKP8nv61oNkjjTsmVLO+qoo+KcxPqvF1UJRfmxdPxEC9wKg7p1N40tkPJDBTbKDz0LjcKJMxK82fyQUmScEa9sfqiVGWekl5HNj7hGRui3RixUwDOZXFZrHHLIIaaRjUxm5513zvQoYa+RqfSdGxMP3UU2HnKrBrEOZEo2b89+3eb89L23at+hQ/KjyGs1qOMas5tvvnnke8mW2ucirtEc53+yP7pWeOo9ZTJxDdrwHfWc4pRrpaOTzeg7KGuaJKyyfbObbBK/Oai+g2x+lEd9VL9+/VgkW265ZdZ4aHQlzmiPk2xpyZa/qt+z+ZGtPtI+K9n8kCyLM7nUR3GNjNBv1Udx31mHHMqwBLi2dc9kNCqZq6nmhGgQ51jz35dddtkGPXH1FK6++mrTkJP2mk83GjZ99913TT1GGQ3Rq6elnlu2lrLca1mdKhINN4aV43PPPWcS2Om9HLnPZAYNGuR7LNkEX6b3820/YMtmttz1crZwH+Cfpn+V7+Dwv8gI3Pj49XbbMwN9rF+8/tWsQ/QVlbw+ffrYvffeu0Hwmew3cIgFBCBQKgKSrf369fOyMd2D2CF6OS5tT1xD1v/5z3/8cJ563uohSWhL4OZi1GJXz3n69OkJAZ98nYsfuIEABCAAAQhUVQJZBbzAaO4iav4i05CkFOzUy9Y8wuDBg/3wtVrymsPQ/G6uRj3922+/3Ss3LFy40DQcf9ttt+X6Ou4gAAEIQAACVZZA/ISRw6IhdSm5aOL/qaeeSvx98MEHGaFJGEvBSnM4WvMurVz1yDWkn01zOtnTK664ws/Db7/99l4x4bjjjjMpoGAgAAEIQAACEIgnkLUHL+UFLTHRPHiyco2UCbRZTZSRZqw0itWTV89fc/Gak5emeLgsLOq9dDspcbzyyit+kxwpjSSHn+6WewhAAAIQgAAEfiUQK+Cl4KZtYvVbEiU1aWpfcskltueee5qWdknrT9damnHzzTf/GnqOV9okBwMBCEAAAhCAQO4EYgW8lmtoCYqWVJTUDBgwwLTpg4y03x9++GHTciNtfICBAAQgAAEIQCC/BGLn4KXFvu+++/rtYcPlbrlER5vUjBo1yh8sI/dqJGiDGw3dx202kYvfuIEABCAAAQhAIDuB2B68lrZpjbuU67RcLtn07NkzcSRsaK/NaWbMmOGXxF166aUpw/pabq8t+LSTU7adq0L/+IUABCAAAQhAoHQEYgW8et5Rm9goqOQd5sKgJcS1B7J6+9quL3k4Xjs8aZeum266KXTOLwQgAAEIQAACeSIQK+AllLWPcK470kmAf/fdd6ataaVkl21v3zylCW8hAAEIQAACVZ5ArIAXndLsSKeev9a7a/28DiTQWvrQaF/hTMvrQjf8QgACEIAABCBQNgKxAr4sO9KVZv182ZLC2xCAAAQgAAEIhARiBXz6jnQacpfynHak0+lw2lkuypR2/XyUX9hBAAIQgAAEIFByArHL5Eq7I11Z1s+XPAm8AQEIQAACEIBAOoFYAZ+8I53mzsMd6bQffI8ePdL9StyXdv18wgMuIAABCEAAAhAoE4FYAS+ftSPdhx9+6APRjnTnn3++X8/eu3fvjAGH6+e1dl77yVerVi3xd8opp2R8jwcQgAAEIAABCJQPgdg5eAWhQ2I0Fz9u3Dh/nruWwkkzXifEde7cOTIWJV0/H+kJlhCAAAQgAAEIlJpAVgGvXedGjx5t7du3TznNTZvYZBLwWj+vI15ffvllv2Wtjo7dbbfd/EY3zZs3L3VkeRECEIAABCAAgdwIxAr4WbNm2RtvvGH6bdGiRW4+/s/VNddc4ze62WGHHWzdunXWqFEj0xGzaix06tSpRH7hGAIQgAAEIACBkhGIFfDaenabbbYpsXDXHPwdd9zhz4TX/L22u9Xe9YsWLbJHH30UAV+yPMI1BCAAAQhAoMQEYpXstt12Wz80/7e//c3Pw+fqu3r8O+20k7Vp0ybllV122cUmTZqUYscNBCAAAQhAAALlTyBSwJ9xxhkJrXcd76p95Rs3bpywk1Z8nDa8huV1qtzrr7+eiPGKFSvsnnvusY4dOybsuIAABCAAAQhAID8EIofoNbzer1+/RIgactdyNxkJ7tatW0eeJhe+UKdOHRsyZIh1797dv1ezZk0bMWKEPz72/fffD53xCwEIQAACEIBAnghECvhmzZqZ/mTuv/9+vzXtnDlzTIL68ssvt7lz5/oz4uPidPrpp/u59ldffdUvq9OueNocR8IfAwEIQAACEIBAfglEDtGHQUr7XRvdjB071gt32Y8cOdIOOOAAf1pc6C7T7+abb+43xrn22mttr732QrhnAoU9BCAAAQhAoJwJxAr4r776yrbYYgvr0KFDItjq1aub1sYnz68nHiZdqOevuXhtlCNzxRVX+GNidUIdBgIJa/bDAAAmjUlEQVQQgAAEIACB/BKIFfAS0DNnzvRr18NorFq1ys+v77zzzqHVBr9l7flv4CEWEIAABCAAAQiUiECsgFdvfdiwYb7HrgNkDjroIL9hzQcffOCPi80UUll6/pn8rKz2PQbf65N2/L1/r6xJJF0QgAAEIFABBGIFvOJz4okn+g1r7rrrLtMpctqo5vPPP7e4LWdL2/OvgPRXeJA7HnGEj8OORxxe4XEhAoVFYPmq5fb9/JmJSE2f/bXfFTJhwQUEIACBGAKRWvTp7rXhjf5yNck9f83hS4P+448/Nh05q13tMBCAQDyBqd9PsUv+0dsmzZiQcDjwiett5PtP2nPXjrJaNWsl7LmAAAQgEEUgaw8+6qVc7NauXWuTJ0+2kvT8c/EXN5WfwJVXXmnffvtt5U9ohhTOXvij/aZve3t/6ju2fNWyhKv5S9wpjlPetuMHHmlLVy5N2HMBAQhAIIpATj34qBfj7KZPn269evWyxYsXx+54F+cHz6ougeeee85/N+lbHVcVIlc9fFnGpK5bv87GTf/YHhs7ws4/6o8Z3fEAAhCAQF568DqgRkfJDh8+3BYsWABlCORMQBsjLVmyxF544QVbs2ZNzu9VFodzF8+1Sd/+Oiwfla4Vq1fYM26oHgMBCEAgjkBkD14Hwvzwww9x7/kT5vbYY49IN9p3Xlvaag97/WlOPjQnn3yyPf744+EtvxBIIXD33Xf7nRIHDhzod02sVatqzTUvWDLfflm3NoVJ1I2G6zEQgAAE4ghECvhHHnnE96D0onpTs2fPtk033dQry0mDfpNNNrGrrrrKMgn4Bg0a2GuvvRYZbv369SPtsYQABMzq161vNTaJLJYpeOpv2iDlnhsIQAAC6QQia5Lbb7/d9Kfz4CXEb731Vj+nrt7UwoUL7YILLvCny6V7Ft6rAaDjYjEQgEDJCGy1RUtr1bSVTZ/zVcYXa1SvYUfsflTG5zyAAAQgIAKxc/Bffvmlaee63//+9xYOlWrZ28UXX2xShMpmXn75Ze9W29Z+9NFHJuU7DAQgkJmAGsfXnNLf9+QzuVLD+7wjUbDLxAd7CEDgvwRiBXyrVq1Me8ePHz8+wUvL36Q8t/vuuyfsoi6uueYaO/fcc/1SuYkTJ/otbzUawHGxUbSwg8CvBPbbqZM9+acXrHH9JlZ9k1/1VzarvZntt1Nnmzr4O9ty8y1/fYErCEAAAhEEIofoQ3c62vW2226zfffd19q1a2datvT222/7TW9eeuml0NkGvzo/XmfKT5s2zT788EO/uU3Pnj1t0aJFfie8Tp06bfAOFhCAwK8EDmx/sL3/l/HW94GL7OVPXvAPLj3uCrvomL7WgPn3X0FxBQEIZCQQ24PXW3369DH1wPWrI1/Ve1cvXEvhMplZs2b5Ofj0dcy77LKLSUMf8yuBWvXq2RVTv/CKi7/aVs2rTz/9NGUa55VXXrHVq1dXTRgu1S0bb227tuqYSP/+Ox+AcE/Q4AICEMhGILYHH77cvn1701+uRnvRa5lc8pGyWjp3zz33WMeOv1ZYufpXmd1pznVLFBLt5ptvtgceeCBlB7vevXv71RpauVG3bt3K/BmQNghAAALlTiAnAS9luVGjRtmuu+5qu+22mzVp0sQvmcsUGw3tDxkyxLp3724NGza0mjVr2ogRI6xp06bMwWeCVoXt1fDTqg3tfJhsNKWj6R7tnfDMM8+YvisMBCAAAQjkRiCrgJeynIbl1SvXOe+NGjWyww8/3J8RHzeXfvrpp5uea2eyefPm+QZBjx49qKRzy5cq40o7HQ4ePHgD4R4CkMa49DjUwDzhhBNCa34hAAEIQCALgdg5+FBZ7r333jMNl8pIWU4KdDo2NpvRYTMaXv3iiy9M8/JaQ4+BQDIB6WSopx5n9N1IwGMgAAEIQCB3ArECvizKcpdddpmdc845NmfOHNtqq6389rTSxpfAx0AgJLB06VKTfkY2w5kG2QjxHAIQgEAqgdgh+tIqy2l722HDhvllcs2aNUuEeN1113lFO218g4GACOj72HzzzW3Zsl+PRY0i07Zt2yhr7CAAAQhAIAOBWAFfWmU5zblrGV2ycFf4hx56qN10000ZooJ1VSSgzY9atmwZe7hR48aNTfobGAhAAAIQyJ1A7BC9juvU1rRTpkyxAQMG+Hn4oUOHmja5iduRbrvttvOnzfXr188vl1u/fr19/PHH1r9/fzv77LNzjx0uKz2B2rVr+yVyzZs3j0yrlsdpNYaOH8ZAAAIQgEDuBCIFvOY7x40b53egu/TSS71ynDa5Oeqoo2z77bf3w+8PPvhgxlCkNKXtbdUo0GY3OkFun3328f6dccYZVq1aNf/31ltvZfSDB1WHwGGHHWbPP/+83y0xPPNAqVdDUbocDz30UNWBQUohAAEIlBOByCF6LU068cQT/dIlzY127do1EZw2ZtE6+Lihds2pamlTNqOhWQwEREANQI3y6FvTwUQyzz77rN97wd/wDwIQgAAESkQgUsBLgH/33Xf+LPhLLrnEr4PPxdc///nP/lhZ7Xqnnn4ms3z5ctNQvxSnjjvuuEzOsK9iBOq5bXs13x6auO2QQzf8QgACEIBANIHIIfrQaYMGDbxwT16//tVXmc+pPvPMM/2w/DHHHGMPP/ywX/+uZVA6gU7vvfjii6aNc9QAWLlypR1xxBFhUPxCAAIQgAAEIFCOBGIFvMLRkjYtl/vll198sFdccYXtv//+/hjZ9Hio1/7EE094If7kk096rXk1EqRIpS1utd+4Dg/54IMP7Oqrr2ZXu3SA3EMAAhCAAATKiUDkEH3ot7amlaLc2LFj/X7ysh85cqQ/AETr3K+//vrQacqvtqgNj5OdP3++6U+NhOrVfz3bOuUFbiAAAQhAAAIQKFcCsT14DatrmVyHDh0SgUpIa0/w5JPiEg8jLjSfr7PkEe4RcLCCAAQgAAEI5IlArIBXr3vmzJn+YJkw/FWrVvmT4nbeeefQil8IQAACEIAABAqMQOwQvXrdGopXj109ea1L1lKmrbfe2q9pL7C0EJ1KQECrK6Tjceedd5r0NzAQgAAEIFA6ArECXl5qPfy0adP8unYdPtO3b1+/s1jyhiSlC5q3ILAhAR1MpG9r2223Ne25gIEABCAAgdIRyKkG/eyzz3yPXTvSqQKWoMdAIF8EtImSdkDEQAACEIBA6QlkFfBat37uueeaznafOHGin5PXASFxe9GXPjq8CQGzU045xe+WCAsIQAACECg9gdgh+p9//tnuuOOOxBD9O++8Yz179jTtNf/oo4+alsNtLKOwO3bs6I8W3VhhEg4EIAABCECgWAnE9uA1FL/TTjttMFy6yy672KRJkzZaml977TXr0qWLHz3YaIESEAQgAAEIQKCICcQKeC2TmzFjRsqa9xUrVtg999zje9P5Trf2rNfOeTqBTgfgYCAAAQhAAAIQyI1A7BB9nTp1/Jp3ncfdsGFDv5vdiBEjrGnTphtlDn7ChAk2b948f/SsDqbBQAACEIAABCCQG4FYAS8vTj/9dD/X/uqrr3phq7XwPXr02Cj7yB9wwAGmvzjzww8/2Pr16zM60YgDBgIQgAAEIFDVCGQV8AIiDfrPP//c7ymvNco6XU7L5QrBPPjgg/4Am0xx0T74GAhAAAIQgEBVI5BVwF922WX+6NeuXbv6zUcef/xxGzRokN++Vse+lpfp06ePDR482Hun/evnzp2bk9eZDrwJX1ZcMRCAAAQgAIGqRiBWwC9ZssRvVaud7Jo1a5Zgc91113lFOx0lW17m2muvtd69e3vvatSIjVZ5BYk/EIAABCAAgUpLIFaSSsFtm222SRHuInHooYeadhsrT6Mh/0IZ9i/PdOEXBCAAAQhAoCIIxC6Tk0JdixYtrF+/fn65nJTZdNhM//797eyzz66I+BImBCAAAQhAAAI5EIgV8Nqxbvz48TZgwAC/2Y32ot9nn338vvRam16tWjX/99Zbb+UQVNmcrFy50nbdddeyecLbEIAABCAAgSpCIHaIfvPNN/enyGVj0bJly2xOeA4BCEAAAhCAwEYkECvgtXtcq1at/AY36XHSNraan8dAAAIQgAAEIFB4BGKH6BcvXmwHHnigffvtt4mYa6hcS9q0hSwGAhCAAAQgAIHCJBDbg9cQvQT8Xnvt5besldLd7373O9tyyy1NG8xgIAABCEAAAhAoTAKxAr569er2l7/8xY488ki/Pe2qVau8wt3VV1/tlesKM0nECgIQgAAEIACB2CF64dFpcrfddps1b97cjj76aBs6dKi9+eabkIMABCAAAQhAoIAJxAr4pUuXWocOHbyi3cSJE+2FF14wbQ17wgkneKFfwOkiahCAAAQgAIEqTSBWwIuMjod94IEHrF69eh6UNrjRMa41a9as0uBIPAQgAAEIQKCQCUQKeG1D+9hjj5k2tlFvffTo0TZlypREOkaNGmWffPJJ4p4LCEAAAhCAAAQKi0CkgJ86dap9//33iZgOGTLE714XWqxbt87Wrl0b3vILAQhAAAIQgECBEYgU8AUWR6IDAQhAAAIQgEAJCSDgSwgM5xCAAAQgAIFiIICAL4ZcIo4QgAAEIACBEhLIuNHN119/bW+88Yb3TufCa14+vP/yyy9LGAzOIQABCEAAAhDYmAQiBbyWwD388MP+L4zMuHHj7L777gtv7aSTTkpccwEBCEAAAhCAQGERiBTww4YNM/1hIAABCEAAAhAoTgLMwRdnvhFrCEAAAhCAQCwBBHwsHh5CAAIQgAAEipMAAr44841YQwACEIAABGIJIOBj8fAQAhCAAAQgUJwEEPDFmW/EGgIQgAAEIBBLAAEfi4eHEIAABCAAgeIkgIAvznwj1hCAAAQgAIFYAgj4WDw8hAAEIAABCBQnAQR8ceYbsYYABCAAAQjEEkDAx+LhIQQgAAEIQKA4CURuVVucSSHWEKh8BP7ca4B9PftL67zzQdZl18MqXwJJEQQgkDcC9ODzhhaPIVA+BKpVq2b6w0AAAhAoCQEEfElo4RYCEIAABCBQJAQQ8EWSUUQTAhCAAAQgUBICCPiS0MItBCAAAQhAoEgIIOCLJKOIJgQgAAEIQKAkBBDwJaGFWwhAAAIQgECREEDAF0lGEU0IQAACEIBASQgg4EtCC7cQgAAEIACBIiGAgC+SjCKaEIAABCAAgZIQQMCXhBZuIQABCEAAAkVCAAFfJBlFNCEAAQhAAAIlIcBe9CWhhVsIVACB2868y+rUqlsBIRMkBCBQzAQQ8MWce8S9ShBo1qh5lUgniYQABMqXAEP05csT3yAAAQhAAAIFQQABXxDZQCQgAAEIQAAC5UsAAV++PPENAhCAAAQgUBAEEPAFkQ1EAgIQgAAEIFC+BBDw5csT3yAAAQhAAAIFQQABXxDZQCQgAAEIQAAC5UsAAV++PPENAhCAAAQgUBAEEPAFkQ1EAgIQgAAEIFC+BBDw5csT3yAAAQhAAAIFQQABXxDZQCQgAAEIQAAC5UsAAV++PPENAhCAAAQgUBAEEPAFkQ1EAgIQgAAEIFC+BBDw5csT3yAAAQhAAAIFQQABXxDZQCQgAAEIQAAC5UsAAV++PPENAhCAAAQgUBAEEPAFkQ1EAgIQgAAEIFC+BBDw5csT3yAAAQhAAAIFQaAoBPyECRNs6tSpBQGMSEAAAhCAAASKgUCNQo7kJ598Yr169bIWLVrYihUrLAgCe/rpp61t27aFHG3iBgEIQAACEKhwAgXdg7/hhhvs/PPPt3feecfGjRtn++67r915550VDo0IQAACEIAABAqdQEH34C+44ALr3LlzgmGbNm1s5MiRiXsuIAABCEAAAhCIJlDQAv6YY45JxHr58uU2dOhQ36NPWLoL9ehXr16dbJVy/f3336fccwMBCEAAAhCoCgQKWsCHGSDhfuKJJ1rr1q2tb9++obX/7d69u61bty7FLvmGHn8yDa4hAAEIQKCqECgYAd+nTx8bPHiw596kSRObO3euv16wYIF169bNmjdvbk8++aTVqJEa5Z122ik2r1577bXY5zyEAAQgAAEIVEYCqdKyAlN47bXXWu/evX0MQiH+008/WdeuXW2PPfawYcOGbSDcKzC6BA0BCEAAAhAoaAIFI+C32mor01+yOeuss/yw/K233mrz5s3zj6pXr25NmzZNdsY1BCAAAQhAAAJpBApGwKfFyyZPnmyjR4/21i1btkw8btWqlc2YMSNxzwUEIAABCEAAAhsSKFgB37FjR7+xzYZRxgYCEIAABCAAgWwECnqjm2yR5zkEIAABCEAAAtEEEPDRXLCFAAQgAAEIFDUBBHxRZx+RhwAEIAABCEQTQMBHc8EWAhCAAAQgUNQEEPBFnX1EHgIQgAAEIBBNAAEfzQVbCEAAAhCAQFETQMAXdfYReQhAAAIQgEA0AQR8NBdsIQABCEAAAkVNAAFf1NlH5CEAAQhAAALRBBDw0VywhQAEIAABCBQ1AQR8UWcfkYcABCAAAQhEE0DAR3PBFgIQgAAEIFDUBBDwRZ19RB4CEIAABCAQTQABH80FWwhAAAIQgEBRE0DAF3X2EXkIQAACEIBANAEEfDQXbCEAAQhAAAJFTQABX9TZR+QhAAEIQAAC0QQQ8NFcsIUABCAAAQgUNQEEfFFnH5GHAAQgAAEIRBNAwEdzwRYCEIAABCBQ1AQQ8EWdfUQeAhCAAAQgEE0AAR/NBVsIQAACEIBAURNAwBd19hF5CEAAAhCAQDQBBHw0F2whAAEIQAACRU0AAV/U2UfkIQABCEAAAtEEEPDRXLCFAAQgAAEIFDUBBHxRZx+RhwAEIAABCEQTQMBHc8EWAhCAAAQgUNQEEPBFnX1EHgIQgAAEIBBNAAEfzQVbCEAAAhCAQFETQMAXdfYReQhAAAIQgEA0AQR8NBdsIQABCEAAAkVNAAFf1NlH5CEAAQhAAALRBBDw0VywhQAEIAABCBQ1AQR8UWcfkYcABCAAAQhEE0DAR3PBFgIQgAAEIFDUBBDwRZ19RB4CEIAABCAQTQABH80FWwhAAAIQgEBRE0DAF3X2EXkIQAACEIBANAEEfDQXbCEAAQhAAAJFTQABX9TZR+QhAAEIQAAC0QQQ8NFcsIUABCAAAQgUNQEEfFFnH5GHAAQgAAEIRBNAwEdzwRYCEIAABCBQ1AQQ8EWdfUQeAhCAAAQgEE0AAR/NBVsIQAACEIBAURNAwBd19hF5CEAAAhCAQDQBBHw0F2whAAEIQAACRU0AAV/U2UfkIQABCEAAAtEEEPDRXLCFAAQgAAEIFDUBBHxRZx+RhwAEIAABCEQTQMBHc8EWAhCAAAQgUNQEEPBFnX1EHgIQgAAEIBBNAAEfzQVbCEAAAhCAQFETQMAXdfYReQhAAAIQgEA0AQR8NBdsIQABCEAAAkVNAAFf1NlH5CEAAQhAAALRBApewAdBYO+99559+umntn79+uhUYAsBCEAAAhCAQAqBGil3BXYzefJkO/bYY61du3b23XffWevWre3ZZ5+1unXrFlhMiQ4EIAABCECgsAgUdA++b9++1qdPHxszZoxNmTLFvvnmG3vqqacKiyCxgQAEIAABCBQggYLuwau3Xrt2bY9tzZo1tmzZMttkk4JukxRgFhMlCEAAAhCoigQKWsA3aNDA58ngwYNt6NChtv/++9vJJ5+ckk/nnXeerVy5MsUu+UYNgl69eiVbcQ0BCEAAAhCo9AQKWsCH9NWL79ixo7377rv2wQcfWJcuXcJHXvAnbiIuBg0aFGGLFQQgAAEIQKByEyiY8W7Ntau3rb+mTZumUD/nnHNs+PDh1rNnT+vfv3/KM24gAAEIQAACENiQQMEI+GuvvdYmTZrk/8aOHWtr1661yy+/3H744YdErNu3b2/Tpk1L3HMBAQhAAAIQgEA0gYIZot9qq61Mf8lm9uzZNnDgQPv73/9uixYtMs3Fd+vWLdkJ1xCAAAQgAAEIRBAomB58RNzsxhtvtDlz5ljbtm1tm2228UP3d911V5RT7CAAAQhAAAIQSCJQMD34pDglLrfbbju/sc3ixYutWrVqFmrVJxxwAQEIQAACEIBAJIGCFvBhjDfffPPwkl8IQAACEIAABHIgUNBD9DnEHycQgAAEIAABCEQQQMBHQMEKAhCAAAQgUOwEEPDFnoPEHwIQgAAEIBBBAAEfAQUrCEAAAhCAQLETQMAXew4SfwhAAAIQgEAEAQR8BBSsIAABCEAAAsVOAAFf7DlI/CEAAQhAAAIRBBDwEVCwggAEIAABCBQ7AQR8secg8YcABCAAAQhEEEDAR0DBCgIQgAAEIFDsBBDwxZ6DxB8CEIAABCAQQQABHwEFKwhAAAIQgECxEyiKw2bKArlFixZ23XXXWY0ahZtUnZbHgTq/5jI8fmWhqxUrVljNmjX9X+qTwrnTaY9Rpl69evbHP/4x6lFB2K1bt85WrlxpiifmvwQof6lfQjHw2G233VIj/b+7aoEzkU+w3GgEfve739m//vWvjRZeoQcEj9Qcuuuuu+zggw+2PffcM/UBd2Um8N1339nQoUNt4MCBZfarsnhA+UvNyWLmwRB9al5yBwEIQAACEKgUBBDwlSIbSQQEIAABCEAglQACPpUHdxCAAAQgAIFKQQABXymykURAAAIQgAAEUgkg4FN5cAcBCEAAAhCoFAQQ8JUiG0kEBCAAAQhAIJUAAj6VB3cQgAAEIACBSkGAdfAFkI2TJ0+2jh07FkBMCiMK8EjNhxkzZtgWW2xhDRo0SH3AXZkJrFq1ymbNmmU77LBDmf2qLB5Q/lJzsph5IOBT85I7CEAAAhCAQKUgUL2/M5UiJRWQiBdeeMGmTZvm/9TLWrJkiTVt2tQ22ST3mY/p06fbk08+aYsWLbLtttuuTKnQpoSZtgwtk8fu5X//+9/WuHFj23TTTVO8evXVV61u3bpWv379FPuom3zGrzR+K79ef/31Cum9afvZUaNG2bbbbpuyBe3LL79sy5cvt+bNmycQfvXVV/4bW716tX3zzTe21VZbJZ5V5YvyKH9vvvmm/7a33HJLa9SoUalxlub7yzWwOXPm2Lvvvmvbb799yiuqM1T+dtpppxT7TDf5imNp/a3I8vf111/b559/bq1atUrgmjt3rq8P9B1sttlmCfu33nrLX8+fP7/4yp/LHEwpCbhcD9q1axfsvffeQfv27QM3hBrsuOOOwfvvv5+Tj26P48ANvQZHHnlkMGzYsJzeyeTotNNOCz755JNMj8ts7/ZCD1xluIE/W2+9dfD4449vYJ9u4QRTcNRRR6Vbl8t9adPuht4C12AplziU1JNffvnFfy9jxoxJvOoEeeAah4HbkjZhpwul75JLLgluueWW4OSTT055VpVvylr+nn322cBV5IHbijTQt1Bak89vW3F67rnngiZNmmwQPdUzYrB+/foNnqVbPPLII8ENN9yQbl3m+7KkvSLLn+uwBA0bNgzcWQQJBjfffLMvfypnoVmzZo2vI955552iLH+5dzV9G4Z/6QQeeOAB+89//mOfffaZLVy40JwQM1cJm1p72Yx6YzroQr3js88+O5vz2OfqDRay0UiFRjvyYQo97VFp1uFHhxxyiO+Zhc9Hjx5tp556qk2dOtXUawvN2LFj7YgjjrCrrrrKnnrqqdCaX0egLOXv448/tuOPP95GjBhhu+66a6l55vPbLnWk0l7UN6SRofI2xZD2qDTrbAfxUC8+NKpHzjrrLD+yFtq5TpM/qGy//fYryvKHgA9zshx+q1evbvfcc48X2hp2D43rnfthNNditO7du5sOuPj000+tR48epmHXDh06mCqbZcuW2QUXXOCHZ1u2bOk/KNfT897o989//rMfptPJV1LKc70//8z18EwnHp100km+sRCGu7F/TzjhBBs+fLi5EQ1/Ot5xxx3nGzoa+jrnnHPs+++/NzfiYTrBSxXrnXfeac2aNbNLL73URzWKkx6UNO2Z/JFft912m2fYtm1bcyMPsqowc9hhh6UIeFUw+iYOOOAAk7CXUQU6b9483xjQoSghq3/84x92/fXXm/JeQ4r6hlSJV2UTVf4ylanbb7/dhgwZYi+99JJ17tzZY1OZVMWvkx0l8F988cUEzpkzZ5q+b02dSOHxmGOOsdmzZ1vUt514aSNeKH5dunSxv/3tb9a6dWsfT9dj9zH45z//aU888YRvDF100UX27bffWteuXe2yyy7z5U/fXSZO8qAkaY/zR34VSvlTHbrvvvsmyp/qzwkTJtiAAQPso48+8vWp4qsyJa5qkCeXv0x1nd4pKBMORfBbcgIuIwM3N7bBi663FbiC5O1dBeKHglwhClyFEFx44YXB7rvvHmjoR0PeGib6+eefg7Vr1wau9Ri4jylwPd3AjQoEe+21V3DjjTd6f1xlFLg5uMBV+IEbKfBDtm3atPHPXKHy/sg/+ZsPk8sQvdK18847B2+//XbgKoXANUICd1SvH0LUMKOb7/JpVfxcBeqnN8RHac3ESUNoJUl7nD+PPvposM022wRuTi2YOHGi51tRQ/Ri4HoPfvhPw/VOmztwlU6wYMGC4I477gh69uwpJ37qxvX0/bWGDl0jzl9rONFVOoGrdIKffvop6Nu3r58e8g+ryL9cyl+mMuVGzgLXWPJlTlNlbj44cI3NoF+/foEbfQucUPRTKG5kztM89NBDA9f4Dty8d+BGWAJ3PGfwf//3f5Hfdnnjz2WIPpzeOeOMMwINm2vqR9/HpEmTAteJ8OlUel2v1cffNYb81ISGqvX9ZOKktJQk7XH+FFr5c+pnwemnn+6z6+mnnw4OP/xwf+0Ef+BGyvy1phXvu+8+f51c/jLVdd5hAf2jB5+H5pZ63+EQvXpaGrLfZ599rFatWnbttdf6IVhX8PwZ1FKKU49BvVS1tNWqdvNtXuFO52irNypz7LHHeoUa9TzdnJu5OX/74Ycf/DMphMgfKbrp3PCKNL1797YDDzzQnCD1cVYPVHFTHKV8mHzu/bnnnmtHH3207/Fn4jR+/PgSpT3OH/XYTzzxRN9L0/nJimtFml122cU0qqOeg3oKulfvUMPxr732mrl6wtvrPspo2FAMpdh55plnegWgKHdVzS4sf1oCl6lM1alTx2rXru3LpJYfSmFP32mfPn38r0baXMPKvy9+OrL3L3/5i/9+1Ztzujb2448/Zvy2K4K56gX1kF3D339D+lX5U72jP6U3VJLVKNpNN93kRxSV/kycSpL2ON7yp9DKn0YxpLwooxGzsJzp95VXXvEjje+9917C3jtM+hdV1yU9LojLGgURi0oWCWnUS6DL6FofSTicLjtp7GpoTxVzaDQMpgLqevi+0gjtNewoexXQa665xt544w1f8buesrcP3eX7Vxr0bqRhg2A0JCehFJpk7W8JdTcyET7a4Fca5KGJ49TaDTnmmvY4f7788ks/xx2GqaHwijbhML3WYocVjIaHJYAUXwl+p2AXGc1k1hpyjGMd6UEltdQ3oPKXrUwlJ1/D1hqmdaNmydamxoKMnmn6RI0xaV6HjewUx3m6UfmSxrkafGqEhEZlTw3mZLtcvwk1ttUIl8nGKde0Z/On0MqfhuilN6WpQwn4iy++2PNQOVTnSnmtujrT6qZk1tnqOu9xBfxDwJczdFXUH374obnhO++zhLg+GLX+Q6MKyGmfmxsmDq38h6SbZ555JtE4kBKICrYKo3oWWlrltDl9BfOWW7ohYb+xjHosUgpMNhqlkNBPLgDJlY3cqlLKZNQTCk0cJzeMlnPa4/zR6IdGTjRvLaMKp6KNehGaA/3iiy/s73//eyI6brjQRo4c6RWB9thjj4R98kUy6zjOye9U9uvk8qfKWSZTmUpmoe9G+iD6xkOuUnRUxa1yqJEm9Xid5r3fcEjlUcJhYxiVPTf15gVRKJQVrpZ6pS+dC+OeLV5yF7qN4yQl4FzTHueP6rBCK3+qf6RzISVLjWiEm41pZEzfUXKvPopnyC98VohlkCH6MHdK+SshJ2UbVQzqpUvR7Te/+Y3XppeXavW7JSoJDXJpQesDWrp0aUqIUpQ66KCD7K9//asX6vrgNPx65ZVXencSpG7exwt3Pbv33nv9R6mehIx6cOG0gLco53/dunWzW2+91Q9pSTHQzbPbeeedZ25JV8pa0kzBavpAjRW9G2XiOJUk7XH+aNhVFbR6Gur9SCGwoo168OqlK077779/IjpqFCqP1QBIr0gSjrjw33ym8petTCXjc0tV/coFfROqqN28tC9vTq/FK83quz3EDdlrOFtKsm5e3Atd+ZHt204OpzTXanionGlIWCMNKkePPfaYOV0Nr/iXi5+KY6b6IY6TBHyuaY/zR3Es1PKncqYGdWgk+FUXSwkzHFULnxXdr/uYMaUk4DLbr0PVr5TQpESm9cpS2gmNlOdk54bYAzfU7JVzpHAn4zTnA1coQqdeOaZTp05e2Urry51QDZwGtX8uBTqtsdd6e9cSDpwGulfQCpWAzj//fL+G8+677074V94XTj8gcMNSPhwphLmPP3B6AIlgpHjiVg8k7qUIFq7blnKS4u2ElX9HSnauh5pwG8epJGmP80eBuWG4wA1r+nXFTrO/wtbBJxLuLqSY6FYVJFt5xSexeuihhxL2yUo+Yhsq4smBlKz0HVYlk0v5k8JZpjIlJTmVm9C4nn7gevKB20gocNNHgZsWCh955Tt9+/rGXQM+uPrqq32+yUH6t514qRwv3GiCV3aTUqjrDQeqH1QeXQPfhxKV/27kJ1EepUzn5uADNy2VULJLjl4cJyke5pr2OH8UXqGVP7dywpebf/3rX8k4Ajea5pUUXeciYZ9c/uLqusQLBXDBVrWultgYRkNsmssKh7HiwlSPVXPuoUJMslsNG2o4UUNe6UZD+Jq7jXqW7rYs94qDFAGTh9hz9U8jF+pNZDJxnEqS9jh/1CvRfHVcPDLFD/viJBBXptJTpO9Mveb0kRMpwqoM69uPMtm+7ah3Smqn0Tstm0ye/83VD8Vf76uOyGQycSpp2jP5o3Apf5nol789Ar78meIjBCAAAQhAoMIJbNgNrPAoEQEIQAACEIAABMpKAAFfVoK8DwEIQAACEChAAgj4AswUogQBCEAAAhAoKwEEfFkJ8j4EIAABCECgAAkg4AswU4gSBCAAAQhAoKwEEPBlJcj7EIAABCAAgQIk8P8BkQhI6eqgdcwAAAAASUVORK5CYII=" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_01.pdf&quot;, 
       width = 9, height = 3.5)


## Figure 02 ----

dat_eff_share_local &lt;- ggpredict(lm_cand_share_local_base,
                                 terms = c(&quot;winner_loser_before&quot;,
                                           &quot;incumbent&quot;))

plot_interaction(dat_eff_share_local)
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_02.pdf&quot;, 
       width = 9, height = 3.5)



## models for general elections: change in support for candidates 
## from government/opposition parties (measured as government status at the time of the election)

lm_govstatus_share_general_base &lt;- estimatr::lm_robust(
    diff_share ~ 
        winner_loser_before * incumbency_status_party,
    clusters = county_gaa,
    se_type = &quot;stata&quot;,
    data = dat_general)



lm_govstatus_share_general_all &lt;- estimatr::lm_robust(
    diff_share ~ 
        winner_loser_before * incumbency_status_party +
        party_recoded + 
        election_id + county_gaa,
    clusters = county_gaa,
    se_type = &quot;stata&quot;,
    data = dat_general)


## Table 02 ----
model_govstatus &lt;- list()
model_govstatus[[&#39;M1: General elections&#39;]] &lt;- lm_govstatus_share_general_base
model_govstatus[[&#39;M2: General elections&#39;]] &lt;- lm_govstatus_share_general_all


rename_coefs_govstatus &lt;- c(&quot;Intercept&quot; = &quot;(Intercept)&quot;,
                            &quot;winner_loser_beforeUntreated&quot; = &quot;Untreated (ref. = Defeat)&quot;,
                            &quot;winner_loser_beforeWin&quot; = &quot;Win&quot;,
                            &quot;incumbency_status_partyParty: Government&quot; = &quot;Candidate&#39;s party in government&quot;,
                            &quot;winner_loser_beforeUntreated:incumbency_status_partyParty: Government&quot; = &quot;Untreated x Candidate&#39;s party in government&quot;,
                            &quot;winner_loser_beforeWin:incumbency_status_partyParty: Government&quot; = &quot;Win x Candidate&#39;s party in government&quot;)


rows_add_govstatus &lt;- tribble(~term, ~m1, ~m2,
                              &#39;Election FE&#39;, &#39;&#39;, &#39;✓&#39;, 
                              &#39;Party FE&#39;, &#39;&#39;, &#39;✓&#39;,
                              &#39;County FE&#39;, &#39;&#39;, &#39;✓&#39;,
)

attr(rows_add_govstatus, &#39;position&#39;) &lt;- c(11, 12, 13)

modelsummary(model_govstatus,
             statistic = &quot;conf.int&quot;,
             fmt = &quot;%.2f&quot;,
             add_rows = rows_add_govstatus,
             stars = c(&#39;*&#39; = .05, &#39;**&#39; = .01, &#39;***&#39; = .001),
             coef_omit = &quot;(party_recoded*)|(election_*)|(county_gaa*)|(Intercept*)&quot;,
             coef_rename = rename_coefs_govstatus,
             gof_omit = &quot;se_type&quot;,
             align = &quot;lll&quot;)
</code></pre>

<table class="table" style="width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:left;"> M1: General elections </th>
   <th style="text-align:left;"> M2: General elections </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Untreated (ref. = Defeat) </td>
   <td style="text-align:left;"> -0.19 </td>
   <td style="text-align:left;"> 0.04 </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> [-0.91, 0.52] </td>
   <td style="text-align:left;"> [-0.73, 0.80] </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Win </td>
   <td style="text-align:left;"> -0.77 </td>
   <td style="text-align:left;"> -0.69 </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> [-1.89, 0.36] </td>
   <td style="text-align:left;"> [-1.51, 0.12] </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Candidate&rsquo;s party in government </td>
   <td style="text-align:left;"> -0.03 </td>
   <td style="text-align:left;"> -0.56 </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> [-1.19, 1.12] </td>
   <td style="text-align:left;"> [-1.77, 0.65] </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Untreated x Candidate&rsquo;s party in government </td>
   <td style="text-align:left;"> -0.94 </td>
   <td style="text-align:left;"> -0.82 </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> [-2.50, 0.63] </td>
   <td style="text-align:left;"> [-2.36, 0.72] </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Win x Candidate&rsquo;s party in government </td>
   <td style="text-align:left;"> 0.76 </td>
   <td style="text-align:left;"> 1.04 </td>
  </tr>
  <tr>
   <td style="text-align:left;box-shadow: 0px 1px">  </td>
   <td style="text-align:left;box-shadow: 0px 1px"> [-0.69, 2.22] </td>
   <td style="text-align:left;box-shadow: 0px 1px"> [-0.56, 2.64] </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Election FE </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Party FE </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> County FE </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Num.Obs. </td>
   <td style="text-align:left;"> 5997 </td>
   <td style="text-align:left;"> 5997 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> R2 </td>
   <td style="text-align:left;"> 0.007 </td>
   <td style="text-align:left;"> 0.075 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> R2 Adj. </td>
   <td style="text-align:left;"> 0.006 </td>
   <td style="text-align:left;"> 0.063 </td>
  </tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; border:0;" colspan="100%">
<sup></sup> * p &lt; 0.05, ** p &lt; 0.01, *** p &lt; 0.001</td>
</tr>
</tfoot>
</table>

<pre><code class="r">modelsummary(model_govstatus,
             statistic = &quot;conf.int&quot;,
             fmt = &quot;%.2f&quot;,
             add_rows = rows_add_govstatus,
             stars = c(&#39;*&#39; = .05, &#39;**&#39; = .01, &#39;***&#39; = .001),
             coef_omit = &quot;(party_recoded*)|(election_*)|(county_gaa*)|(Intercept*)&quot;,
             coef_rename = rename_coefs_govstatus,
             gof_omit = &quot;se_type&quot;,
             align = &quot;lll&quot;,
             output = &quot;tab_02.docx&quot;)
</code></pre>

<pre><code>## [1] &quot;tab_02.docx&quot;
</code></pre>

<pre><code class="r">## Figure 03 ----

dat_eff_govstatus_share &lt;- ggpredict(lm_govstatus_share_general_base,
                                     terms = c(&quot;winner_loser_before&quot;,
                                               &quot;incumbency_status_party&quot;))


dat_eff_govstatus_share$winner_loser_untreated &lt;- factor(dat_eff_govstatus_share$x,
                                                         levels = c(&quot;Defeat&quot;, 
                                                                    &quot;Untreated&quot;, 
                                                                    &quot;Win&quot;))


dat_eff_govstatus_share$group &lt;- relevel(factor(dat_eff_govstatus_share$group),
                                         ref = 2)


ggplot(dat_eff_govstatus_share, 
       aes(x = winner_loser_untreated,
           y = predicted,
           colour = winner_loser_untreated)) +
    geom_hline(yintercept = 0, colour = &quot;grey30&quot;, size = 1, 
               linetype = &quot;dashed&quot;) +
    facet_grid(~group) +
    geom_linerange(aes(ymin = predicted - 1.96 * std.error,
                       ymax = predicted + 1.96 * std.error),
                   size = 0.5) +
    geom_linerange(aes(ymin = predicted - 1.645 * std.error,
                       ymax = predicted + 1.645 * std.error),
                   size = 1.3) +
    geom_point(size = 4) + 
    scale_y_continuous(limits = c(-3, 3),
                       breaks = c(seq(-3, 3, 1))) +
    scale_colour_manual(values = c(&quot;darkred&quot;, &quot;black&quot;, &quot;darkgreen&quot;)) +
    labs(x = NULL, y = &quot;Expected change in vote share\n(percentage points)&quot;) +
    theme(legend.position = &quot;none&quot;)
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_03.pdf&quot;, 
       width = 9, height = 3.5)



## Robustness: Regression Models ----

## rerun models using absolute changes in votes

lm_cand_votes_general_base &lt;- estimatr::lm_robust(
    diff_votes ~ 
        winner_loser_before * incumbent,
    clusters = county_gaa,
    se_type = &quot;stata&quot;,
    data = dat_general)


lm_cand_votes_local_base &lt;- update(
    lm_cand_votes_general_base, 
    data = dat_local)

lm_cand_votes_general_all &lt;- estimatr::lm_robust(
    diff_votes ~ 
        winner_loser_before * incumbent +
        party_recoded + election_id + county_gaa,
    clusters = county_gaa,
    se_type = &quot;stata&quot;,
    data = dat_general)


lm_cand_votes_local_all &lt;- update(
    lm_cand_votes_general_all,
    data = dat_local)


## Table A3 ----
models_votes &lt;- list()
models_votes[[&#39;M1: General elections (votes)&#39;]] &lt;- lm_cand_votes_general_base
models_votes[[&#39;M2: General elections (votes)&#39;]] &lt;- lm_cand_votes_general_all
models_votes[[&#39;M3: Local elections (votes)&#39;]] &lt;- lm_cand_votes_local_base
models_votes[[&#39;M4: Local elections (votes)&#39;]] &lt;- lm_cand_votes_local_all


modelsummary(models_votes,
             add_rows = rows_add_4,
             statistic = &quot;conf.int&quot;,
             fmt = &quot;%.1f&quot;,
             stars = c(&#39;*&#39; = .05, &#39;**&#39; = .01, &#39;***&#39; = .001),
             coef_omit = &quot;(party_*)|(election_*)|(county_gaa*)|(Intercept*)&quot;,
             coef_rename = rename_coefs,
             gof_omit = &quot;se_type&quot;,
             align = &quot;lll&quot;)
</code></pre>

<table class="table" style="width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:left;"> M1: General elections (votes) </th>
   <th style="text-align:left;"> M2: General elections (votes) </th>
   <th style="text-align:left;"> M3: Local elections (votes) </th>
   <th style="text-align:left;"> M4: Local elections (votes) </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Untreated (ref. = Defeat) </td>
   <td style="text-align:left;"> 262.7 </td>
   <td style="text-align:left;"> 297.7 </td>
   <td style="text-align:left;"> 4.7 </td>
   <td style="text-align:left;"> -20.0 </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> [-110.1, 635.6] </td>
   <td style="text-align:left;"> [-106.0, 701.4] </td>
   <td style="text-align:left;"> [-49.3, 58.7] </td>
   <td style="text-align:left;"> [-68.7, 28.8] </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Win </td>
   <td style="text-align:left;"> 235.6 </td>
   <td style="text-align:left;"> 14.9 </td>
   <td style="text-align:left;"> 24.3 </td>
   <td style="text-align:left;"> 20.4 </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> [-173.6, 644.9] </td>
   <td style="text-align:left;"> [-518.0, 547.8] </td>
   <td style="text-align:left;"> [-21.7, 70.3] </td>
   <td style="text-align:left;"> [-50.8, 91.7] </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Candidate elected in t-1 </td>
   <td style="text-align:left;"> -519.6* </td>
   <td style="text-align:left;"> -643.9* </td>
   <td style="text-align:left;"> -57.4*** </td>
   <td style="text-align:left;"> -80.4*** </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> [-1003.4, -35.8] </td>
   <td style="text-align:left;"> [-1170.0, -117.7] </td>
   <td style="text-align:left;"> [-84.7, -30.0] </td>
   <td style="text-align:left;"> [-116.0, -44.8] </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Untreated  ×  Candidate elected in t-1 </td>
   <td style="text-align:left;"> -328.0 </td>
   <td style="text-align:left;"> -314.2 </td>
   <td style="text-align:left;"> -36.3 </td>
   <td style="text-align:left;"> -22.8 </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> [-836.0, 180.0] </td>
   <td style="text-align:left;"> [-855.7, 227.4] </td>
   <td style="text-align:left;"> [-98.1, 25.5] </td>
   <td style="text-align:left;"> [-82.3, 36.7] </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Win  ×  Candidate elected in t-1 </td>
   <td style="text-align:left;"> -428.3 </td>
   <td style="text-align:left;"> -377.7 </td>
   <td style="text-align:left;"> -35.6 </td>
   <td style="text-align:left;"> -23.0 </td>
  </tr>
  <tr>
   <td style="text-align:left;box-shadow: 0px 1px">  </td>
   <td style="text-align:left;box-shadow: 0px 1px"> [-932.9, 76.2] </td>
   <td style="text-align:left;box-shadow: 0px 1px"> [-928.1, 172.6] </td>
   <td style="text-align:left;box-shadow: 0px 1px"> [-88.6, 17.4] </td>
   <td style="text-align:left;box-shadow: 0px 1px"> [-81.1, 35.0] </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Election FE </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Party FE </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> County FE </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Num.Obs. </td>
   <td style="text-align:left;"> 5997 </td>
   <td style="text-align:left;"> 5997 </td>
   <td style="text-align:left;"> 8674 </td>
   <td style="text-align:left;"> 8674 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> R2 </td>
   <td style="text-align:left;"> 0.031 </td>
   <td style="text-align:left;"> 0.105 </td>
   <td style="text-align:left;"> 0.009 </td>
   <td style="text-align:left;"> 0.078 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> R2 Adj. </td>
   <td style="text-align:left;"> 0.030 </td>
   <td style="text-align:left;"> 0.094 </td>
   <td style="text-align:left;"> 0.009 </td>
   <td style="text-align:left;"> 0.071 </td>
  </tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; border:0;" colspan="100%">
<sup></sup> * p &lt; 0.05, ** p &lt; 0.01, *** p &lt; 0.001</td>
</tr>
</tfoot>
</table>

<pre><code class="r">modelsummary(models_votes,
             add_rows = rows_add_4,
             statistic = &quot;conf.int&quot;,
             fmt = &quot;%.1f&quot;,
             stars = c(&#39;*&#39; = .05, &#39;**&#39; = .01, &#39;***&#39; = .001),
             coef_omit = &quot;(party_*)|(election_*)|(county_gaa*)|(Intercept*)&quot;,
             coef_rename = rename_coefs,
             gof_omit = &quot;se_type&quot;,
             align = &quot;lll&quot;,
             output = &quot;tab_a03.docx&quot;)
</code></pre>

<pre><code>## [1] &quot;tab_a03.docx&quot;
</code></pre>

<pre><code class="r">## Figure A08 ----
dat_eff_votes_general&lt;- ggpredict(
    lm_cand_votes_general_base,
    terms = c(&quot;winner_loser_before&quot;,
              &quot;incumbent&quot;))


plot_interaction(dat_eff_votes_general,
                 axis_title = &quot;Expected change in votes (absolute)&quot;) +
    scale_y_continuous(limits = c(-800, 1300))
</code></pre>

<pre><code>## Scale for &#39;y&#39; is already
## present. Adding another
## scale for &#39;y&#39;, which will
## replace the existing scale.
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_a08.pdf&quot;, 
       width = 9, height = 3.5)


## Figure A09 ----
dat_eff_votes_local &lt;- ggpredict(lm_cand_votes_local_base,
                                 terms = c(&quot;winner_loser_before&quot;,
                                           &quot;incumbent&quot;))

plot_interaction(dat_eff_votes_local,
                 axis_title = &quot;Expected change in votes (absolute)&quot;) +
    scale_y_continuous(limits = c(-250, 250))
</code></pre>

<pre><code>## Scale for &#39;y&#39; is already
## present. Adding another
## scale for &#39;y&#39;, which will
## replace the existing scale.
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_a09.pdf&quot;, 
       width = 9, height = 3.5)



## Descriptive plots on elections and candidates for Supporting Information ----

dat_general_describe &lt;- select(dat_general, -year_lag)

dat_general_describe$type_election &lt;- &quot;General elections&quot;
dat_general_describe$election_year &lt;- factor(dat_general_describe$election_year)
dat_general_describe$elected &lt;- factor(dat_general_describe$elected)

dat_local_describe &lt;- select(dat_local, -year_lag)

dat_local_describe$type_election &lt;- &quot;Local elections&quot;
dat_local_describe$election_year &lt;- factor(dat_local_describe$election_year)
dat_local_describe$elected &lt;- factor(dat_local_describe$elected)


dat_merged &lt;- bind_rows(dat_general_describe, dat_local_describe)


dat_sum_general &lt;- dat_general_describe %&gt;% 
    ungroup() %&gt;% 
    filter(!is.na(diff_share)) %&gt;% 
    group_by(winner_loser_before) %&gt;%
    summarise(n = n()) %&gt;%
    mutate(relfreq = n / sum(n)) %&gt;% 
    mutate(type = &quot;General elections&quot;)

dat_sum_local &lt;- dat_local_describe %&gt;% 
    ungroup() %&gt;% 
    filter(!is.na(diff_share)) %&gt;% 
    group_by(winner_loser_before) %&gt;%
    summarise(n = n()) %&gt;%
    mutate(relfreq = n / sum(n)) %&gt;% 
    mutate(type = &quot;Local elections&quot;)


dat_sum_combined &lt;- bind_rows(dat_sum_local, dat_sum_general)

dat_sum_combined$winner_loser_before &lt;- factor(
    dat_sum_combined$winner_loser_before, 
    levels = c(&quot;Defeat&quot;, &quot;Win&quot;, &quot;Untreated&quot;)
)

## Figure A03 ----
ggplot(dat_sum_combined, aes(x = winner_loser_before,
                             y = relfreq, fill = winner_loser_before)) +
    geom_bar(stat = &quot;identity&quot;, width = 0.6) +
    geom_text(aes(label = n), nudge_y = 0.04, size = 4) +
    scale_fill_manual(values = c(&quot;darkred&quot;, &quot;darkgreen&quot;, &quot;grey30&quot;)) +
    scale_y_continuous(limits = c(0, 1), labels = scales::percent_format(accuracy = 1)) +
    facet_wrap(~type) +
    labs(x = NULL, y = &quot;Percentage of observations within group&quot;) +
    theme(legend.position = &quot;none&quot;)
</code></pre>

<p><img 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Qj4o++2F1980Tp06GBDhw51D41foU6zOr9jS/4Bq8mRL9GLPoccVgXK7t27u2Dlta+5E0QfbgVtVQPqC0FVaEq9evUyr43N9TD32tbtt99+cwFt3Lhxrjd5WkgUhHQSeW1lrvf9woULzWtXcz8UUnq/erNre+o9rJNdAc7rUOO+2FI6+WPXlZ5tqqe+vgC9Nn936aCq+FU6VkBVj3YlOTz//PPuagC97vUbcAPZqLe7knq3KxD7zRxupvdHJfHevXu7XvDy1ReM1/7vjG+44QZ/scD/Kv0PGDDABXTVCGjMAq/jnasVCXwjLyaUgMZR0Oc89qHj7HWwcz8GFZB07qjHt3+liX6sKwV9Pk/0XErvd0Bq556+e/R9M2jQIFMT1rfffmteB11r0aKF25eUziW9qHPJ6/TnetV7HVxNTYuPP/64q5U87bTT3PuD/mjdXudXu//++933j85HDTCkH/GqLczVyftiJeUQAa8K2/Wk9wa1iXhVf66TijrTee1bEe/6+CR7+f7770e8y0lcz231JPfavCJeMHPL+J1atIyf/Hl+Rz4vILrOQNqOV43metx7l/xEO9mok53WG5u8LzbXU1jvUWczrx3adYLzTkDXGS95x57Y92o6tW16QTZJL/rVq1dHvKYA15lNeVSPfr+job8+r93OdWxS5ya9HtuLXevzviTcw99/vxe9V60Y8b6Y3HvV+91r8nBXB/h5Vic7bTM2eT+gkszzSuuRM844wx0rr+TibPwe1LHvYzrxBPxOdvpsJ3/oyhQlr19MxGtCcq/r8+f1V3EdLf291ec9pc/niZ5L2kbQd0DyczG1c0/r837wR7zaMXdO6HzwfhS4c1avJT+XYjvZ6XWdG3qPziV9P6hHvfdDQS9FO9kFfR95l7S6K1S82jN3jnm1c+7KBLeCXPwnj/Y9V//CyaE7752QrlSg9ubYtt/ku6uSo6rg9Cv4eJLa27UODYsbtJ3YdasvgJbVdo8npXebqgpVjYXfUTD5NlU1qhJE8nZALadqQlnqWvd4Sa+r9JWe/U++nk2bNrkqf1X7k3KfgNqv9TlSs1e8FPT5PNFzSdtLz3dAWs493QPD69Ue7Yvi71Nq55KW03vVTBjb3u6/Py3/1Z4vT22f5NU2EuD5GCCAAAIIIJDzBGiDz3nHlD1CAAEEEEDACPB8CBBAAAEEEMiBAgT4HHhQ2SUEEEAAAQQI8HwGEEAAAQQQyIECuX6gmyFDhrjrNnPgsWWXEMg2AQ0uctdddx2zfQ0n6o90eMyLzEAAgeMS0LgfGuo6ecr1AV4DK2iwFRICCGScQOwY/7Fr1XDDGlyIhAACGSOgkUA1imm8AE8VfcYYsxYEEEAAAQRCJUCAD9XhIDMIIIAAAghkjAABPmMcWQsCCCCAAAKhEiDAh+pwkBkEEEAAAQQyRoAAnzGOrAUBBBBAAIFQCRDgQ3U4yAwCCCCAAAIZI0CAzxhH1oIAAggggECoBAjwoTocZAYBBBBAAIGMESDAZ4wja0EAAQQQQCBUAgT4UB0OMoMAAggggEDGCBDgM8aRtSCAAAIIIBAqAQJ8qA4HmUEAAQQQQCBjBAjwGePIWhBAAAEEEAiVAAE+VIeDzCCAAAIIIJAxApka4HXf5ylTpsTN6fz5802PePeG3rBhg3366ae2c+fOJO/VrV3HjRtne/bsSTJ/8uTJtmLFiiTzeIIAAggggEBuFsjUAK97Qt93331JfLdu3Wp169a1u+66yzp06GAtW7ZMErB79OhhTZo0sYceeshq165tCxcudO9fvny5XXLJJbZ+/Xpr2rRp9IeBfgT079/fqlatmmQ7PEEAAQQQQCA3C2RKgP/hhx/s0ksvtX//+9/H2CrgX3nllS5wr1y50vLkyWMvvPCCW+7DDz+0zz77zH766SebM2eODRo0yHr27OlemzhxovtB0K9fP6tfv77NmDHDzR85cqT16tXLChcu7J7zBwEEEEAAAQTMMiXAv/3223b11VfbP/7xj2OMp02bZrfccoubX6hQIevatau9//777rle69ixoxUrVsw97969u6vG37hxo5UtW9b279/v5quKvkSJEqYqewV+LUdCAAEEEEAAgaMC+Y9OZtzUkCFD3Mo++uijJCs9cOCA/fLLL1a5cuXo/EqVKtmvv/7qnq9Zs8ZatGgRfa148eJWpEgRU4Bv06aNPfnkk+617du32znnnGMqzd9///1WoECB6HtiJxYtWmQq4Qcl1SCQEEAAAQQQyGkCmRLgU0JS+3skEomW0LWcAvju3bvdW7Zs2WJFixZN8na9rhJ7xYoVXWleVffDhw93QV/V+SNGjLAFCxbYhAkT7KabbnLV9/4KGjdubG+++ab/NO7/u+++O+58ZiKAAAIIIJDIAplSRZ8SSLly5Vyb+44dO6KLaNrvIFe+fPljes6rE53/uqruW7VqZfny5bNhw4bZgAED7LvvvrNu3brZBRdcYG3btrW9e/dG180EAggggAACuVUgSwN8/vz5rUqVKrZq1aqot6Zr1qzpnteoUcPU8c5Pa9eutSNHjli1atX8We7/6tWrbd68eda5c2d3GV67du1ccD/vvPPc5XVJFuYJAggggAACuVAgSwO8fG+99VZXra7qeF369tJLL9nNN9/s6PXa+PHjXfBWSVxt+V26dDH9MIhNQ4cOtcGDB1vevHld6d6v4ldpX7UEJAQQQAABBHK7QJYHeHWMUzt8nTp17KKLLjKVvq+99lp3HBo1auQ6zTVv3txq1aplKqk/88wzSY7RsmXLTI/27du7+a1bt3aX1vXp08fUxq9SPAkBBBBAAIHcLpC0aJzBGrpUTo/YVKpUKfvPf/7jgrF6ySfvAT9w4EB74IEHXMe60qVLx77VTR8+fNhee+216Hwto8Fw9HjuuedcG3/0RSYQQAABBBDIpQKZGuCDTMuUKZPiywULFjQ94qUGDRocM1vLqpMdCQEEEEAAAQT+K5DlVfTAI4AAAggggEDmCxDgM9+YLSCAAAIIIJDlAgT4LCdngwgggAACCGS+AAE+843ZAgIIIIAAAlkuQIDPcnI2iAACCCCAQOYLEOAz35gtIIAAAgggkOUCBPgsJ2eDCCCAAAIIZL4AAT7zjdkCAggggAACWS5AgM9ycjaIAAIIIIBA5gsQ4DPfmC0ggAACCCCQ5QIE+CwnZ4MIIIAAAghkvgABPvON2QICCCCAAAJZLkCAz3JyNogAAggggEDmCxDgM9+YLSCAAAIIIJDlAgT4LCdngwgggAACCGS+AAE+843ZAgIIIIAAAlkuQIDPcnI2iAACCCCAQOYLEOAz35gtIIAAAgggkOUCBPgsJ2eDCCCAAAIIZL4AAT7zjdkCAggggAACWS5AgM9ycjaIAAIIIIBA5gsQ4DPfmC0ggAACCCCQ5QIE+CwnZ4MIIIAAAghkvgABPvON2QICCCCAAAJZLkCAz3JyNogAAggggEDmCxDgM9+YLSCAAAIIIJDlAgT4LCdngwgggAACCGS+AAE+843ZAgIIIIAAAlkuQIDPcnI2iAACCCCAQOYL5M/8TbAFBBBIi8C+ffssEolEFy1UqJDlyZMn+nzTpk1WtGhR99DMI0eO2P79+6Ov582b1woWLBh9vmXLFjv55JMtX7580XlMIIBA7hGgBJ97jjV7GmKBgwcPumBcvXp18x8rV66M5njz5s127rnn2tdffx2d995771np0qWjy7ds2dK99ttvv1mHDh2sdevWdvrpp9vYsWOj72ECAQRyjwAl+NxzrNnTEAt8//33duaZZ9q8efOOyeWkSZOsd+/etmvXriSvLVq0yIYNG2b9+vVLMv/VV1+1ypUr24QJE2zPnj1Wt25du+iii9wPgSQL8gQBBHK0ACX4HH142blEEVCwPuuss+yLL75IUkpX/hcuXGgTJ060M844I8nu6D2nnXaaffjhh6bqez999NFHdt1117mnqtI/++yzTcuSEEAgdwkQ4HPX8WZvQyqgAKzA/O6779rdd99tl156abQ9fvDgwdawYcNjcq73PPHEEzZ16lRr1qyZ/fWvf3XLtGjRwqZNm+am1a4/Z84ci63uP2ZFzEAAgRwpQBV9jjys7FSiCfTt29f69+/vqtaV98aNG9uMGTNcoE9pX9QG36RJEzvppJNcFf7FF19sffr0sTvvvNO6dOniAr+q9Zs2bWrFixdPaTXMRwCBHCpACT6HHlh2K7EEDh8+nCQIq7p+6dKlKe6EOuWp+l3BXalOnTqm0rqq6mvVqmVfffWVvfnmm7Z48WIrVaqU1a5dO8V18QICCORMAQJ8zjyu7FWCCYwaNcoeffRRl+sdO3aYOta1b98+xb3Q5XPXXHONa5/XQmqHV3u8OteNGDHCVd2rc5161KsqX6V4EgII5C4Bquhz1/Fmb0MqoOr57t2723nnnWerVq2yP//5z4G93vPnz2/PP/+89ezZ0+2RLqMbN26cm7799ttdJzu16aukP3r0aCtZsmRI95xsIYBAZgkQ4DNLlvUikA6BihUruk52ajMvXLhwtOo9dhWff/557FNr27ate2zbts1dD++/WLZsWZs5c6bt3LmTwO6j8B+BXChAgM+FB51dDq9AiRIl0p05DXYTL1Fqj6fCPARyjwBt8LnnWLOnCCCAAAK5SIAAn4sONruKAAIIIJB7BAjwuedYs6cIIIAAArlIgACfiw42u4oAAgggkHsECPC551izpwgggAACuUiAAJ+LDja7igACCCCQewQI8LnnWLOnCCCAAAK5SIAAn4sONruKAAIIIJB7BAjwuedYs6cIIIAAArlIgJHsctHBZlezT6B58+bZt/E0bnnWrFlpXJLFEEAgEQQowSfCUSKPCCCAAAIIpFOAAJ9OMBZHAAEEEEAgEQQI8IlwlMgjAggggAAC6RQgwKcTjMURQAABBBBIBAECfCIcJfKIAAIIIIBAOgUI8OkEY3EEEEAAAQQSQYAAnwhHiTwigAACCCCQTgECfDrBWBwBBBBAAIFEECDAJ8JRIo8IIIAAAgikU4AAn04wFkcAAQQQQCARBAjwiXCUyCMCCCCAAALpFCDApxOMxRFAAAEEEEgEAQJ8Ihwl8ogAAggggEA6BQjw6QRjcQQQQAABBBJBgACfCEeJPCKAAAIIIJBOAQJ8OsFYHAEEEEAAgUQQIMAnwlEijwgggAACCKRTgACfTjAWRwABBBBAIBEECPCJcJTIIwIIIIAAAukUIMCnE4zFEUAAAQQQSAQBAnwiHCXyiAACCCCAQDoFCPDpBGNxBBBAAAEEEkGAAJ8IR4k8IoAAAgggkE4BAnw6wVgcAQQQQACBRBAgwCfCUSKPCCCAAAIIpFOAAJ9OMBZHAAEEEEAgEQQI8IlwlMgjAggggAAC6RQgwKcTjMURQAABBBBIBAECfCIcJfKIAAIIIIBAOgUI8OkEY3EEEEAAgcQXiEQitm7dOjty5MgxO/Pbb7/ZgQMHjpm/b98+27lzZ3S+1rF3795jHpofhpSmAP/+++/bPffcY127drUnn3zSfvnllzDknTwggAACCCCQboFly5ZZw4YNrVu3bla/fn375JNP3DoUvC+++GLr0qWL1a1b115//fXouocMGWJNmjSxyy+/3G677TbzfyDUqFHD/EfVqlWtaNGitnbt2uj7snMi1QB///332+23326//vqrVa5c2caNG2fNmjWz7777LjvzzbYRQAABBBA4LoH77rvPBg4caJ9++qmLac8//7xbz1NPPWUXXXSRTZ8+3WbOnGn33nuvHTp0yFatWmVjxoyxr7/+2hYsWGAHDx60F154wapXr26bN2+OPv70pz/ZHXfc4QL+cWUsg9+UP2h9u3btsldeecV+/PFHq1ChQnTRv/zlL/bss8/aiy++GJ3HBAIIIIAAAmEXUFz74osvTDXTCtZnnHGGffTRRy7bDz/8sB0+fNhNqxReqFAhy5cvn3v9qquussKFC7vXWrdu7Ur9d999d3R3v/rqK3vvvfds4cKF0XnZPRFYgtcvk2rVqiUJ7srwZZdd5n7RZHfm2T4CCCCAAALpEVi/fr2dfPLJ1rx5c1O1u6ro33nnHbcKBfMCBQq45mhVxY8ePdry5Mnjqu0///xzV5rXgtOmTbOVK1cm2eyAAQNs0KBBVqxYsSTzs/NJYICvXbu2VapUyfSrZs2aNa4zwvz58x2K2i5ICCCAAAIIJJLAH3/84drIVR2vkvv48eNt2LBhSXZBVfYTJkxw1e2qnm/cuLGdf/75duqpp1rTpk1Nne1KlCgRfc/y5cvt+++/t86dO0fnhWEisIp++/btrs1h6tSp9sgjj1iRIkVcb0FlfNasWXbzzTe7fVB7xSWXXOKm+YMAAggggEBYBapUqWIFCxa0Cy64wGVRwfunn34yxbsNGza4WuvSpUvb1Vdf7drjZ8yYYSrsvvzyy6be9Srlq31e7fd+evXVV12HPa03TCkwwJcsWdLmzp2ban4FRkIAAQQQQCDsAhUrVnTt7pMmTXJB/LPPPnMdyFVtP3z4cBfAH3vsMfv9999t3rx59re//c31Q1Nnc7Xd67I69T/r27dvdFfV/v7AAw9En4dlIjDA582b1+rUqROWvJIPBBBAAAEETljgn//8p2tnVyDXZd/vvvuuW6eCdPfu3V11vHrKDx482PwCrK4eO/fcc01x8corr7S2bdtG86Hq+QYNGkSfh2UiMMBrB/3rA5NnWJfMnXXWWcln8xwBBBBAAIFQCzRq1Mi+/fZb27Jli5UtWzaa1/Lly9sHH3zgSu/FixePztfE008/7ZqoTzrpJNMjNuky8jCmwAC/Z88e69evXzTfuh5QPRDVy1A9BtMb4FW1sXTp0uj6/An1UtSgA0rqzKeqET+pveP00093T9WrX9Up11xzjRtMwF9m8uTJrqZBAxOQEEAAAQQQSItAbHCPXT55cPdfUz+0REqBAb5UqVKmEX9ik4L8LbfcYrVq1YqdnaZp9V5s1apVkmV3797tfg3t2LHDzb/22mvd8IH+LyT1VFyxYoWpl2KHDh1cRwb1YtSvr/z587thA/v372/q3U9CAAEEEEAAgf8KBF4mFw9JQbVnz55uVJ94rwfN0xB+6oXoP9RzUVX9Gv5Waf/+/e5SAwVvfxkFd6WJEye6AK8aBV23qJ6NSiNHjrRevXpFByBwM/mDAAIIIIBALhcILMHHs1EQ/ve//+0umYv3enrmaRhcXaJw5513urcpsKsNRKPmaUjAU045xVSLoKSqFFXRK6npQCV7PVfgD9PIQS6D/EEAAQQQQCCbBQID/LZt29xAN34eNbi+Ot7pGsGPP/7Yn31c/3VZwWuvvZZkUP5vvvnGVI2v3oiqutf2NZKQmgTatGkTLenresVzzjnH9Q/QjwT1CYiXVO3/ww8/xHspOk/7Q0IAAQQQQCCnCQQGeF0Hr5J0bNKF/Bq+9kQv6H/ppZesY8eOrsTur1+ldF2i8Oijj7prETXevQbv15CBulRB7exz5sxx1ypu3LjRdbgbMWKEG09Yow7ddNNNrvreX5/uDKQfDUFJfQpICCCAAAII5DSBwACvHuwqTb/11lumQQFUNa7LCzQcX7169Y7bQqXzt99+260zdiXt27c3Pfyktn4Nk6vArgCvMX79TnoaWlA9+XVXOw2bqwEKdF2iqvn9no66hZ/a54NS7M0CgpbjNQQQQAABBBJJINVOdrrwX0FSJV0Fe12S1qJFi7iXu6V1x7WOcuXKucH+Y9+joB9b9a/2fj2SDyCwevVqN8KQfmhMmTLF2rVr54L7eeedl2T4wNh1M40AAggggEBuEggM8GrDfu6551wJWqV4lZo1dK1KzKNGjTpuJ10L36RJk2PerxGC9GNCbe9qG9fNANRjPvn17UOHDnUjDGn5qlWruvZ6rUxV8vrhQEIAAQQQQCC3CwQG+E2bNrngmny4Wg00s2TJkuO2U8e35KVyrey6665zl8Ip+Ctwa/jA119/Pcl2dF2+Hn5Vvu7Lq8Fv+vTpY1u3bjWV4kkIIIAAAgjkdoHANnjdGk8953W3uEsvvdRZqapePdvjBei0Yvrj/iZfXiXyZ555xpXcFax1yVzydPjwYdf73p+vHv26TE4P1TZoVDwSAggggEDuFdC93sOcdDfWrEiBAV4ZUEc2dWxTNbkCvgaY0TB+abnL3PHugDr3xQvuWl+8Hxaxt/473m3yPgQQQAABBHKSQKoBXj3Sv/zyS5s9e7apyl4d2tS5TaPSkRBAAAEEEEAgnAKBAX7VqlXWpUsX13nt7LPPDucekCsEEEAAAQQQOEYgsJOdBrS58MIL3bjzahMnIYAAAggggEBiCAQG+L1797rbt/bt29eNBa8bzfgPlexJCCCAAAIIIBBOgcAqet3Q5dNPP42b85Tulxt3YWYigAACCCCAQJYKBAZ4XRIX7z7ruhRNnezUsz5er/Ys3QM2hgACCCCAAALHCAQGeA0Tq9HkFi1a5G7hqsFndPMWVdPrVq5r1651A8xoHHgSAggggAACCIRHILANXpfI6cYw7733nunubQsWLHCXyjVt2tTdunX58uU2duxYU297EgIIIIAAAgiERyAwwCuAq7TeoUOH6AhxZcqUcWPRv//+++4Ob1dccYUbRS48u0ROEEAAAQQQQCAwwKsafv369a6K3qfSULHjx49394TXMLYaF143hCEhgAACCCCAQHgEAtvgNQTs448/bueee641btzYKleu7Iaq1X3WdXMX3a9dw8qeeeaZ4dkjcoIAAggggAACFhjg5dO7d293/3eNQb9r1y4bMmSIC/Z6TePU16pVS5MkBBBAAAEEEAiRQKoBXnlt2LCheyTPN8E9uQjPEUAAAQQQCIdAYBt8OLJILhBAAAEEEEAgvQIE+PSKsTwCCCCAAAIJIECAT4CDRBYRQAABBBBIr0CqbfBHjhxx94LfvHmzadpPGtXu/PPP95/yHwEEEEAAAQRCJJBqgG/durXNmTPHatasaQUKFIhmvWXLlgT4qAYTCCCAAAIIhEsgMMD/9NNP7mYz+l++fPlw5ZzcIIAAAggggECKAoFt8BroRreMLVeuXIor4AUEEEAAAQQQCJ9AYICvUqWKNWvWzF544QXbuXNn+HJPjhBAAAEEEEAgrkBggN+xY4fNmjXLjWZXqlQpd8MZ3Qtej+uvvz7uCpmJAAIIIIAAAtkvENgGr+p5Bfh4qVixYvFmMw8BBBBAAAEEQiCQYoDXneLy5s1rtWvXDkE2yQICCCCAAAIIpEcgbhX9zTffbJ06dbJt27a5IK9An/zRuXPn9GyHZRFAAAEEEEAgCwXiluCfeuopUwle7e6rVq2Km52iRYvGnc9MBBBAAAEEEMh+gbgBPvaa92rVqtnixYtt9erVdujQoWiOq1evbhUqVIg+ZwIBBBBAAAEEwiMQN8DHZq9Dhw42ZcoUa9CgQZKR7K644gq78MILYxdlGgEEEEAAAQRCIhAY4NetW2fTpk0z/a9UqVJIskw2EEAAAQQQQCA1gbid7Pw3qR1eVfQEd1+E/wgggAACCCSGQGCAVzu7quaff/5516M+MXaJXCKAAAIIIIBA3ACvy+T8EesmTJhgffv2tTJlykTnMZIdHxwEEEAAAQTCLRC3DX7kyJH28MMPB+ackewCeXgRAQQQQACBbBWIW4LX5W916tQxVdGvWLHCTeu5/8iXL599+eWX2ZpxNo4AAggggAACKQvELcFv3brV1qxZY7rZzD333JPkXvDqePfOO+/Y999/b7qEjoQAAggggAAC4ROIG+AVxDt27OhuEbt7927TNe9+0pC1ZcuWtUcffdSfxX8EEEAAAQQQCJlA3ACvAP7zzz/brl27XAe7MWPGhCzbZAcBBBBAAAEEggTiBni9QaV43S721VdfddPxVqLe9CQEEEAAAQQQCJ9A3E523E0ufAeKHCGAAAIIIJAegbgleP9ucrpj3OzZs+OOZMfd5NLDzLIIIIAAAghkrUDcAO/fTW7nzp125ZVXWv369a19+/buoZHtSAgggAACCCAQboG4VfR+lkuWLGlbtmyxoUOH2oYNG6xNmzZWt25d69+/vy1atMhfjP8IIIAAAgggEDKBwACvvBYqVMgFdo1H/+6779o555xjqsJ/7LHHQrYrZAcBBBBAAAEEfIG4VfT+iwcPHrQXXnjB3TJ25syZphHudE38+PHj7dJLL/UX4z8CCCCAAAIIhEwgMMBrkJt+/fpZ8eLF7b777rNbb73VatasGbJdIDsIIIAAAgggkFwgsIr+5JNPdm3wr7zyim3atMlatmxptWvXth49etj06dOTr4vnCCCAAAIIIBASgcASvPKojnbqQd+uXTv7+uuv7eWXX7Z//vOfptI91fQhOYpkAwEEEEAAgWQCgQH+0KFD9uabb9onn3xin376qauqv/baa+0///mPNW/ePNmqeIoAAggggAACYREIDPAai37UqFGmoD5o0CA744wzwpJv8oEAAggggAACAQKBAb506dL21VdfBbydlxBAAAEEEEAgjAKBnezCmGHyhAACCCCAAAKpCxDgUzdiCQQQQAABBBJOgACfcIeMDCOAAAIIIJC6QKoBfuHChW5oWq3queeeM92IpnXr1qYOeCQEEEAAAQQQCKdAYIDXZXK6/v3AgQOmO8tpVDvdeKZMmTI2ZMiQcO4RuUIAAQQQQAABCwzwq1atsrJly9qDDz5okyZNsqpVq1rPnj3dsLULFiyADwEEEEAAAQRCKhAY4PPmzetGrFPeJ06caB06dHC7sXz5cjvttNNCuktkCwEEEEAAAQQCr4PXvd91B7lGjRrZypUrbe7cue5OcirFjxs3Dj0EEEAAAQQQCKlAYIBXnjUs7eTJk90odvXr13ft8Rr8pl69eiHdJbKFAAIIIIAAAqkG+GLFilmnTp2iUk2bNo1OM4EAAggggAAC4RRINcAfOXLEZs+ebZs3bzZN+0kd7s4//3z/Kf8RQAABBBBAIEQCqQZ4XfM+Z84cq1mzphUoUCCadd0bngAf5WACAQQQQACBUAkEBviffvrJ5s+fb/qvAW5ICCCAAAIIIJAYAoGXyRUsWNBKlChh5cqVS4y9IZcIIIAAAggg4AQCA3yVKlWsWbNm9sILL7iR7DBDAAEEEEAAgcQQCAzwO3bssFmzZlnv3r2tVKlSlidPnujj+uuvT4w9JJcIIIAAAgjkQoHANnhVzyvAx0u6fI6EAAIIIIAAAuEUCAzwGqq2Tp069tZbb7mx6Pfs2eNGtevcubNVrFgxnHtErhBAAAEEEEAg+GYz8nnggQesV69epjvLNWjQwI1q16JFC1u6dCl8CCCAAAIIIBBSgVTb4HUPeF0qp1L8sGHD3Hj03bp1s1GjRoV0l8gWAggggAACCAQG+E2bNpluOKNq+th0zTXX2JIlS2JnMY0AAggggAACIRIIDPCnnnqqRSIRmz59ejTLqqofPXq0q66PzmQCAQQQQAABBEIlENjJTjkdMGCAtWrVypXkFfBnzJhhxYsXd1X1odoTMoMAAggggAACUYFUA/xtt91mZ511lk2bNs1UZd+uXTtTL/qiRYtGV8IEAggggAACCIRLIMUAr6p5DWyj/2eeeaZ7xGbdfz12HtMIIIAAAgggEA6BuG3wN998s7sH/LZt20zXwsd7qBRPQgABBBBAAIFwCsQtwT/11FOu5F6kSBH75z//aZdcckmS3K9bt87dYS7JTJ4ggAACCCCAQGgE4pbg8+XLZ+vXr7cvvvjChg8fbtu3b48+VKr/+OOPbfz48aHZCTKCAAIIIIAAAkkF4pbg1b7esWNHdwe53bt32xVXXBF9l6rry5Yta48++mh0HhMIIIAAAgggEC6BuAFeAfznn3+2Xbt2Wd++fW3MmDHhyjW5QQABBBBAAIFAgbgB3n+H7ib30ksvuYFudu7c6WYfPnzY9u7da0eOHLFbb73VX5T/CCCAAAIIIBAigcAAr3xefvnlNm/ePCtcuLCp053uEa/R7DRGPQkBBBBAAAEEwikQGODV0W7BggX2yy+/uJvL6B7wd999t3Xt2tUqVaoUzj0iVwgggAACCCAQfLtY9ZjXIDelS5e2Jk2auF71+fPntwcffNBeeeUV+BBAAAEEEEAgpAJxL5Pz81q7dm378ccf3SVzDRs2tNmzZ9vBgwft999/t3379vmL8R8BBBBAAAEEQiYQWEWv8ebVi15j0Wsc+nr16rnpDRs22GOPPRayXSE7CCCAAAIIIOALBAZ4LfTII4+YbjijNHHiRHvttdesfv36Sa6Ndy/yBwEEEEAAAQRCIxAY4A8cOOCq50855RSXYV0216dPn9BknowggAACCCCAQHyBwDb4P/74wxo0aODGolfJfc+ePfHXwlwEEEAAAQQQCJVAYIAvWbKkbdy40W688UZ7+eWX3aVxd9xxh+tsF6q9IDMIIIAAAgggkEQgMMBryVKlSlmPHj1s1qxZtnjxYqtSpYq1bNnSunfvnmRFaX2ice6XLl2a5KFOe7FJzz/99FM3Fn7s/M2bN9u4ceOOqUmYPHmyrVixInZRphFAAAEEEMjVAqkGeOlo5DrdQW7QoEH27LPP2qWXXmo33HDDccEtX77cGjdu7Drp6SY2ejz99NPRdenHhK65f+ihh0yX6S1cuNC9pvfptrUafKdp06YuT3pBQ+j279/fqlatGl0HEwgggAACCOR2gcBOdvv377d+/frZ22+/7Ury3bp1syeffNKV4o8XbtGiRXb++efHreb/8MMP7bPPPnP3mteoeX/961+tZ8+ebqhc9eDv0KGDy49uYztjxgz342DkyJHWq1cvN5Tu8eaJ9yGAAAIIIJDTBAIDvAaz+fbbb121+GWXXZYh+/7NN9/YOeec4+4vrzvWqRPfSSed5NY9bdo0d5taBXclNQPcf//9rh+A7nCnKnoldfZTj349V+D3S/nuRf4ggAACCCCAgAUG+C1bttjMmTPtgw8+yDAqBXi1l3/yySe2Zs0aq1atmqnkfuqpp7rnLVq0iG6rePHi7gY36ujXpk0bV3ugF7dv3+5+JKh2QT8AChQoEH1P7IRqC1TCD0p58uQJepnXEEAAAQQQSEiBwACv4HvhhRe6+8GrJ32ZMmVOeCc1Kp6q+rt06eLuTNe5c2dXDa+qef2g0Oh5sUl3sFOJvWLFijZ//nybM2eODR8+3JXq9Z4RI0a4G+JMmDDBbrrpJjcIj/9+tfW/+eab/tO4/3XzHBICCCCAAAI5TSCwk53u+65StoarVRW5bjTjPxSgjyc9/vjjLrjrveqhrzZ2jXGv+8uXL1/+mJ7z6kTnd6BT1X2rVq0sX758NmzYMBswYIB999137gfDBRdcYG3btnX3qj+efPEeBBBAAAEEcpJAYAle7dy6XC1eUvV5epN64yswq21dtQNKu3btcqXuvHnzWo0aNWzlypXR1a5du9YFfn9Z/4XVq1e7jnd///vfXRV8u3btXHD/17/+5fKr5yQEEEAAAQRys0BggFfQPe200zLMR6X/H374wQX5UaNGuU5y//jHP6xTp05uG7feeqtdfvnlpsF0zjjjDBsyZIgr7et9sWno0KE2ePBgU/5Uuvevo1dpv1y5crGLMo0AAggggECuFAisovdFJk2a5Magf/HFF+2rr76yVatW+S+l+////d//uTvTaXx7XedevXp1u/fee916GjVq5DrNNW/e3GrVqmUqqT/zzDNJtrFs2TLTo3379m5+69at3aV1GiN/69atdt555yVZnicIIIAAAgjkRoGkReM4AgMHDnSd7OrWrWuHDx+2k08+2Y1kN2XKFFO7d3qTesvr0jZVzatkrk50sUnbe+CBB1zHutKlS8e+5KaVB42L7ycto8vk9HjuueeMXvG+DP8RQAABBHKzQGCA37Fjh2vj/vHHH23u3LluuFpVp+sytbFjxx5XgPex1b6fUipYsKDpES/puvnkScsez4+N5OvhOQIIIIAAAjlFILCKft26da4NXtXlsen0009349LHzmMaAQQQQAABBMIjEBjgVS2/xrtMTteb+0mXzmk8+oYNG/qz+I8AAggggAACIRMIrKIvVKiQjR492l2CpmvWNaTsG2+84a5X14AzJAQQQAABBBAIp0BggFeWu3bt6tq3p06d6i5rU8939WBX8CchgAACCCCAQDgFAqvolWX1Tn/vvffcPeE1uI1GtVOAVy94EgIIIIAAAgiEUyAwwGvkOY0Kd+DAATeErG7uokFmNCa9BqEhIYAAAggggEA4BQIDvAa00Rj0Dz74oGmwG40ap7Hj77vvPneDl3DuErlCAAEEEEAAgcAAr6Fgd+/e7ZQ0OE2HDh3c9PLlyzN0CFsOAwIIIIAAAghkrEBgJztdJlehQgXTELK6CYwGuxk/frwrxY8bNy5jc8LaEEAAAQQQQCDDBAIDvLbyn//8xyZPnuxu/lK/fn3XHq/x6OvVq5dhmWBFCCCAAAIIIJCxAqkG+KJFi7r7r2ucd92tTXeX69GjR8bmgrUhgAACCCCAQIYKBLbBa0t33XWX3X777fbrr79apUqV3I1iVHr/7rvvMjQjrAwBBBBAAAEEMk4gsAS/adMmdyc5tb9Xq1YtulX1qn/kkUfs7bffjs5jAgEEEEAAAQTCIxBYgleVvAK7Lo+LTR07drQtW7bEzmIaAQQQQAABBEIkEBjg1YteQ9M+/PDDpjvLRSIR++mnn+z//u//7M477wzRbpAVBBBAAAEEEIgViBvgb7nlFtM18HpoDPphw4ZZ9erVTUPVKuB//PHHSe4wF7tCphFAAAEEEEAg+wXitsGPGDHC/vKXvwTmTsGehAACCCCAAALhFIgb4CtWrGh6+Omtt95yQ9Xu2bPHDXrTuXNn16Pef53/CCCAAAIIIBAugbhV9LFZfOCBB6xXr16mG880aNDADXrTokULW7p0aexiTCOAAAIIIIBAiAQCA/yOHTtMA9zMnz/fVIpXW7yGq+3WrZuNGjUqRLtBVhBAAAEEEEAgViAwwOs6ePWkr1OnTux77JprrrElS5YkmccTBBBAAAEEEAiPQGCAP/XUU92lcdOnT4/mWFX1o0ePdtX10ZlMIIAAAggggECoBOJ2sovN4YABA6xVq1auJK+AP2PGDHe5nKrqSQgggAACCCAQToFUA/xtt91mZ511lk2bNs1UZd+uXTtTL3rdhIaEAAIIIIAAAuEUSDXAK9sNGzZ0j3DuArlCAAEEEEAAgeQCgW3wyRfmOQIIIIAAAggkhgABPjGOE7lEAAEEEEAgXQJxA/yRI0fStRIWRgABBBBAAIFwCcQN8Oo1r051Gpp2+PDh4coxuUEAAQQQQACBVAXidrIrWbKkvfzyy7Z161Z76aWX7Oyzzz5mRRUqVLAzzzzzmPnMQAABBBBAAIHsF4gb4AcOHGh9+/a1/v3728aNG61Hjx7H5LRNmzb2t7/97Zj5zEAAAQQQQACB7BeIG+CbNm1qX3zxhWks+htuuMHdYCb7s0oOEEAAAQQQQCCtAnEDvP/mUqVKueAe73ax9erV8xfjPwIIIIAAAgiETCBuJ7vYPHK72FgNphFAAAEEEEgMgcAAz+1iE+MgkksEEEAAAQSSCwQGeG4Xm5yL5wgggAACCCSGQGCA53axiXEQySUCCCCAAALJBQI72WlhbhebnIznCCCAAAIIhF8g1QDP7WLDfxDJIQIIIIAAAskFUg3wegO3i03OxnMEEEAAAQTCLRDYBh/urJM7BBBAAAEEEEhJgACfkgzzEUAAAQQQSGABAnwCHzyyjgACCCCAQEoCaWqDnzRpkhuyVnePa9SokZUtW9Zq166d0jqZjwACCCCAAALZLJBqCV53luvevbstWbLEFi1aZGvXrrUmTZrYnDlzsjnrbB4BBBBAAAEEUhIILMFrqNqRI0fajz/+aHPnzrVZs2ZZp06dbPv27TZ27Fi74IILUlov8xFAAAEEEEAgGwUCS/Dr1q2z0047zWrVqpUki6effrotXrw4yTyeIIAAAggggEB4BAIDfN26dW3NmjX22WefRXO8d+9ee/bZZ9218dGZTCCAAAIIIIBAqAQCq+gLFSpko0ePtrZt25ruDX/SSSfZG2+8YeXLl6cNPlSHkcwggAACCCCQVCAwwGvRrl27urb2qVOn2ubNm13v+fbt25uCPwkBBBBAAAEEwikQGOAPHDhg77zzjst5kSJFrEaNGnbo0CEbP368FS1a1FSF36BBg3DuGblCAAEEEEAgFwsEBvj9+/fbU0895S6Pq1ChglWtWtW++eYby58/v51yyinukrk+ffrY8OHDczEhu44AAggggED4BAI72anUvnv3bnvvvfds48aNtmDBAtu0aZM1bdrUnnzySVu+fLm7XG7VqlXh2zNyhAACCCCAQC4WCAzwCuAqrXfo0MHy5MnjmMqUKWPdunWz999/36pUqWJXXHGFLVy4MBcTsusIIIAAAgiETyAwwKsafv369a6K3s/64cOHXRt8tWrVLBKJ2LJly6x+/fr+y/xHAAEEEEAAgRAIBLbBFyxY0B5//HE799xzrXHjxla5cmWbMWOG62yntveePXtavnz5TGPUkxBAAAEEEEAgPAKBAV7Z7N27t7Vo0cIF9l27dtmQIUNcsNdrAwYMOGaUO80nIYAAAggggED2CqQa4A8ePGhbt2616tWru5yqQ93SpUvtyJEjduutt2Zv7tk6AggggAACCMQVSDXAX3755TZv3jwrXLiwqVe9bkCja+Gfe+65uCtkJgIIIIAAAghkv0BggFcHO10a98svv9ioUaOsWLFidvfdd7vR7SpVqpT9uScHCCCAAAIIIBBXILAX/bZt21wHutKlS7t7wH/xxRfusrkHH3zQXnnllbgrZCYCCCCAAAIIZL9AYICvXbu2uxe8SvINGza02bNnm9rkf//9d9u3b1/2554cIIAAAggggEBcgcAqeo0337dvXzvrrLPcCHb16tVz0xs2bLDHHnss7gqZiQACCCCAAALZLxAY4JW9Rx55xG677TaX04kTJ9prr73mBrbRCHYkBBBAAAEEEAinQGAVve4mN3nyZHdjGWW/RIkSpgFuVHU/YcKEcO4RuUIAAQQQQAABi1uC13Xva9ascZfE3XPPPVa+fPkolYan1S1kv//+ezdGffQFJhBAAAEEEEAgNAJxA7yCeMeOHW3nzp3ubnKx1fF58+a1smXL2qOPPhqanSAjCCCAAAIIIJBUIG6AVwD/+eefTUPTqpPdmDFjkr6LZwgggAACCCAQaoG4Ad7Psdrcdb37559/bps3b3bD0/qvVa1a1c4//3z/Kf8RQAABBBBAIEQCgQFe+WzdurXNmTPHatasaQUKFIhmvWXLlgT4qAYTCCCAAAIIhEsgMMD/9NNPNn/+fNP/2I524doFcoMAAggggAACyQUCL5PT/eBVTV+uXLnk7+M5AggggAACCIRYIDDAV6lSxZo1a2YvvPCC61Ef4v0gawgggAACCCAQIxAY4HVr2FmzZlnv3r2tVKlSlidPnujj+uuvj1kNkwgggAACCCAQJoHANnhVzyvAx0u6dSwJAQQQQAABBMIpEFiC16A2derUseXLl9uzzz5r06ZNM41yp5J8xYoVw7lH5AoBBBBAAAEELDDAy2fgwIHWvXt3W7JkiS1atMjWrl3r7g2vS+dICCCAAAIIIBBOgcAqerXBjxw50t0Tfu7cua66vlOnTrZ9+3YbO3asXXDBBeHcK3KFAAIIIIBALhcILMGvW7fOTjvtNKtVq1YSptNPP90WL16cZB5PEEAAAQQQQCA8AoEBvm7duu6ucp999lk0x3v37nXt8Q0bNozOYwIBBBBAAAEEwiUQWEVfqFAhGz16tLVt29ZdJnfSSSfZG2+84Ua1ow0+XAeS3CCAAAIIIBArEBjgtWDXrl1dW/vUqVPdDWdq165t7du3NwV/EgIIIIAAAgiEUyDVAK9slyxZ0nr06OH2YMWKFQT3cB5LcoUAAggggEBUILANXku9+OKLprb4gwcPujf169fP3UVu/fr10ZUwgQACCCCAAALhEggM8IcPH7ZHHnnEZs6caWp/V3rvvffsoosucveJD9eukBsEEEAAAQQQ8AUCA7yq40uXLm1nnHGGv7zly5fPOnToYLE966MvMoEAAggggAACoRAIDPCqmtfIdVOmTIlmdt++fa5nff369aPzmEAAAQQQQACBcAkEdrJTaf2VV15xJXaV5NWDfv78+Va1atUUb0ITrt0jNwgggAACCOROgcAAL5JDhw65cei/+eYb08h29913n7suvkCBArlTjL1GAAEEEEAgAQQCA/yqVausS5cutnPnTsvo+7/rB0PhwoWtXr16SZjWrFljv//+e3SeahE0NK7S5s2bXdv/NddcY0WLFo0uM3nyZHfXOzUpkBBAAAEEEEDALDDAV6tWzS688EIbM2aM3Xh4mk1FAAAhXklEQVTjjVamTJkTNluwYIH70VCpUiXTsLeRSMT+/e9/2ymnnOLWfe2117qaAr/Xvu5Jr85+umWtOvd169bNmjZtat9++63lz5/f/fjo37+/azo44cyxAgQQQAABBHKIQGAnOwVglaj79u1rZcuWdQFVQVUPleyPJw0dOtQNmjNr1ixbuHChNWvWzJ5++mm3qv3799v333/vgvdvv/1meii4K02cONEFeF2Hrw5+M2bMcPN1t7tevXq52gA3gz8IIIAAAgggEFyCV+n5008/jctUvHjxuPNTm/mnP/3J1Qr4y+lOdbq2Xkml8vLly1uFChXs66+/dqX6UqVKudf0A0NV9Ep79uwx5U3PFfj1Q4GEAAIIIIAAAkcFAqvo8+bN624XO2nSJFM795lnnmmNGjVypfnKlSsfXUs6pq6++uro0grUL730UnQYXLXL//HHH9agQQPbvXu3bdu2zV2Sd8stt1ibNm3sySefdO/V/ejPOeccU2n+/vvvt5Q6/G3atCnVqnvVGpAQQAABBBDIaQKBVfTa2YEDB1r37t1dT/pFixa56+KbNGliJ3o3OQX3jh07Ws2aNV3PfG1LpXRta+nSpW47jz76qKnEv2HDBqtYsaIL1i1btrS5c+faxo0bXYc7BX+16w8aNMh++OEHrSaadAWAOuwFPdQHgIQAAggggEBOEwgswe/YscPUxv3jjz+6oKp2806dOplK0GPHjnV3mTsekK1bt9pVV13lgvY777zj2vS1Ht2lTg8/9ezZ0x5++GEX2KtUqWLFihWzVq1auZeHDRtmAwYMsO+++851vBs+fLi7fE/V/EWKFHHLqJYhtb4C2icSAggggAACOU0gsASv695PO+00Uzt5bNJla4sXL46dleZpdZy75JJL3OVxanuPve3s22+/bR9//HF0Xao+10NV9rFp9erVNm/ePOvcubMbZa9du3YuuJ933nkp9hmIfT/TCCCAAAII5HSBwACv68rViz523Hn1rH/22WetYcOGx2Vz2223uWp5lbjVSU5V7WorV1Kbv3rEq+1dd6976qmnXI/55Ne3qyf+4MGD3fIaVU/t9Uq6Xr9cuXJumj8IIIAAAgjkZoHAKnqVrkePHu1Kx+rNrmvT33jjDdfT/Xja4JcsWRId115V7n6qUaOG+yFx3XXX2RdffGFq49eY9xoeV6X62LRs2TLTw6/Kb926tT322GPWp08fU9W/SvEkBBBAAAEEcrtAYIAXTteuXV1b+9SpU12JW+PRK7jGVq2nFVGl/qBObSrBP/PMM67krmCtS+aSJ93C9rXXXovO1o8AXSanx3PPPWd58uSJvsYEAggggAACuVUgMMCrmlxJbfA9evTIMiMNTxsvuCsDydvjNa9gwYLH3eFP7ychgAACCCCQ0wTitsGrlKzR60qWLGmqStcNZnbt2pXT9p39QQABBBBAIMcKxC3Bq3f766+/bq+++qqpNK1rzDWYzBNPPJFjIdgxBBBAAAEEcpJA3AD/+eefuyp5XYamtH79etP16iQEEEAAAQQQSAyBuFX0qo7XqHJ+0rCwupyNhAACCCCAAAKJIRA3wB85ciRJb3RdHqd2eRICCCCAAAIIJIZA3Cp6ZV2jxc2ePdvthW7hqhHl/OeaqRJ+vXr13Ov8QQABBBBAAIFwCcQN8LqWfNSoUe4Rm93mzZtHn2pMetrloxxMIIAAAgggECqBuFX06kGvAWmCHgT3UB1HMoMAAggggEASgbgBPskSPEEAAQQQQACBhBMgwCfcISPDCCCAAAIIpC5AgE/diCUQQAABBBBIOAECfMIdMjKMAAIIIIBA6gIE+NSNWAIBBBBAAIGEEyDAJ9whI8MIIIDA8QscOHDAfvnll2NW8Ntvv5leS540Bope85Ourtq7d+8xj6Bbgfvv5X/WChDgs9abrSGAAALZKqBbfw8fPjyah507d9rFF19sXbp0sbp167objfkv/u1vf7P69eubxkDp2LGj+wGwbt06q1GjRvRRtWpVK1q0qK1du9Z/G/9DIkCAD8mBIBsIIIBAZgooMF9xxRX2ySefJNnMU089ZRdddJFNnz7dZs6caffee68dOnTI1qxZY3pt0aJFptFMNVz5uHHjrHr16rZ58+bo409/+pPdcccdLuAnWTFPsl0g7kh22Z4rMoAAAgggkKECX3/9td122222fft2W7FiRXTdDz/8cPReIyqFFypUyN0mfN68ea5kX6JECbesRi+dMmWK3XLLLdH3fvXVV6bbiy9cuDA6j4nwCFCCD8+xICcIIIBApglce+21dtNNNx2z/nz58lmBAgWsa9eudvnll9vo0aPdzcZ0P5IKFSpEly9fvvwxdxUdMGCADRo0yIoVKxZdjonwCBDgw3MsyAkCCCCQbQLPP/+8TZgwwVW3r1q1ynRX0dikTnS6s6ifli9f7qruO3fu7M/if8gECPAhOyBkBwEEEMhKgW+//dbU0a506dJ29dVXu/b4GTNmWOXKlW3Tpk3RrGi6Vq1a0eevvvqqdevWzQoWLBidx0S4BAjw4Toe5AYBBBDIUoE33njDnnjiCbfN33//3dT23rp1a2vZsqVNmzbNfv31V3e78LFjx9pll10WzZva31u0aBF9zkT4BOhkF75jQo4QQACBLBN44IEHrHv37nb++efbwYMHbfDgwValShW3/fvuu88aNGhg5cqVcx3u1NHOT+pZr9dI4RUgwIf32JAzBBBAIMMF+vTpk2Sd6jz3wQcfmErvxYsXT/KaLpm76667XC97Xesem1SyJ4VbgAAf7uND7hBAAIEsEUge3P2N6rI5UmIK0AafmMeNXCOAAAIIIBAoQIAP5OFFBBBAAAEEElOAAJ+Yx41cI4AAAgggEChAgA/k4UUEEEAAAQQSU4AAn5jHjVwjgMBxCmzZsiU69rpWoRurJL/96b59+5Ksff369cbtUJOQ8CQBBAjwCXCQyCICCJy4gO5p3qFDBzeIy+mnn24auEVJY6/H3v7Uv+Zbr3333Xd26qmn2nXXXWc1a9a0ZcuWaTYJgYQQIMAnxGEikwggcKICGlpVw68uWLDAdGe1fv36uXuY9+7dO3rrU5XUNRzr0KFD3eZ0DfgzzzxjGrVtyJAh1r9//xPNBu9HIMsECPBZRs2GEEAgOwU++ugjVxJXHjRoy9lnn+3udR6bp4ceesguvfRSV8pX1f3SpUvtqquucovceOONNnXq1NjFmUYg1AIE+FAfHjKHAAIZJaBx0zW2upLa2OfMmWMrV66Mrn7jxo02atSoaOl93bp17gYsefLkccvopip6bNu2LfoeJhAIswAj2YX56JA3BBDIMIE777zTunTp4krhu3btsqZNmyYZmlVV+BprXXdVUzp8+PAx205+y9RjFsjGGcU6hbu8tvvdpLefzUaqXLNpAnyuOdTsKAK5W0Bt62pLX7FihetUd/PNN1vt2rWjKK+88oqNGzcu+lzt9epxr/ui582b1/744w8rUKBAkh8F0YWZQCCEAuH+yRdCMLKEAAKJKTBixAh3W9S6deuaetQvWrTIleK1N1u3bnW3RVWp3k9FihRxr48fP97NGjNmjGuf91/nPwJhF6AEH/YjRP4QQCBDBG6//XbXyU6d7XRbVF0eV7JkSbdu3fpUl8757e3+Bp9++mlr166dPfLII679XXddIyGQKAIE+EQ5UuQTAQROSKBs2bI2c+ZM27lzZzSw+yts3ry5zZs3z38a/d+kSRN3Kd327dujbfPRF5lAIOQCVNGH/ACRPQQQyFgBv9Se1rWqVO93vEvre1gOgTAIEODDcBTIAwIIIIAAAhksQIDPYFBWhwACCCCAQBgECPBhOArkIYmAejTv3r07ybwDBw7Yjh07kszzn3AjEF+C/wgggMBRAQL8UQumQiCgAUg0hKh6OvvpySeftGbNmtnVV19tnTt3dtcl6zVuBOIL8R8BBBA4VoAAf6wJc7JRoE+fPrZ///5oDtR7+a9//avNnj3bPfQD4OOPP3avcyOQKBMTCCCAwDECBPhjSJiRXQLvvvuuGzHs4osvjmZBI4j9/vvvpkCva5cV4FVdz41AokRMIIAAAnEFCPBxWZiZ1QIbNmyw4cOH27PPPptk07qk6YUXXnBDilaqVMmqV69uHTt2NG4EkoSJJwgggMAxAgT4Y0iYkdUCuoFHt27dXFV8iRIlkmxe7ex/+ctfbNKkSW6Qkl9++cVeeumlhLsRSJKd4gkCCCCQBQKMZJcFyGwiWED33J4xY0b01p2bNm1yt/Xcs2eP6znftm1bu/zyy91KHnjgAVfKv+mmm7gRSDArryKAQC4XIMDn8g9AGHb/jDPOSHKPbZXmFdS7du1qGiNcN/tQ+/tJJ53kOtpddtllFnsjEN3ikxuBhOFIkgcEEAiTAAE+TEcjl+ZFHemKFSsW3fv8+fNboUKFXEBv1KiRtW7d2q644grXu75OnTr25z//2S3LjUCiZLl2YkCefKHe9ycix95TPtQZJnM5SoAAH4LDqVtXnnzyye5e0352VGJVT3E/qfSqwOcnvadChQr+0xz1/6233kqyPw899JBrh9+3b58rufsvciMQX4L/CCCAwLECdLI71iTL5uiuVrokrEuXLqZ7VL/++uvRbauN2e81rp7jfu/yr776yg0Ec+ONN9r5559v3377bfQ9OXlCpXxVyydP3AgkuQjPEUAAgf8KEOCz8ZPw1FNP2UUXXWTTp093PcTvvffeaKl90aJFtnjxYtu8ebN7qHOZ0qOPPmqDBg2yzz77zHr06GEjRozIxj1g0wgggAACYRU4Wucb1hzm4Hw9/PDD0cu91q5d69qd8+XL58Zh17jrunxMo7a1atXKtUeLQvN0qZjSli1bkoz65mbyBwEEEEAAAU+AAJ+NHwMFcz3UW/ydd95xvcVV5bxkyRI3ctvgwYPdD4DevXvb+++/bw0bNrRRo0aZ2p4fe+wxNya7SvkkBBBAAAEEkgtQRZ9cJBueP//88zZhwgS74447bNWqVabLxhYuXGhvvPGGjR071rp3726jR492OWvfvr3df//9NnfuXDc4jH4ckBBAAAEEEEguQIBPLpKFz9VBTh3tSpcu7e6UpvZ4DfiiHvSlSpWK5qRp06amwWA0PKseAwYMsGrVqtnQoUNNne50e1USAggggAACsQIE+FiNLJ5WCf2JJ55wW9UNVebNm+eu+V6xYoUbue3w4cOuGl6l+A4dOljVqlWtXLlyrpSvN6kj3imnnGJlypTJ4pyzOQQQQACBsAvQBp+NR0g941X9rsvdVGpXm3uVKlXc47rrrjOV3Pfu3Wunnnqqq75X+7wGd9EtVTVf7ffPPPNMNu4Bm0YAAQQQCKsAAT4bj0z58uXtgw8+cLdDLV68eJKcPP744zZs2DAXyGNvwKIe9XqoxJ/8PUlWwBMEEEAAgVwtQBV9CA5/SoFaI9fFBvfYrKb0nthlmEYAAQQQyL0CBPjce+zZcwQQQACBHCxAFX0OPrjZvWvFOoX/9+Pud49kNxPbRwABBDJFIPzfwJmy26wUAQQQQACBnC1AgM/Zx5e9QwABBBDIpQIE+Fx64NltBBBAAIGcLUCAz9nHl71DAAEEEMilAgT4XHrg2W0EEEAAgZwtQIDP2ceXvUMAAQQQyKUCBPhceuDZbQQQQACBnC3AdfAZfHwH5MmXwWvM+NU9ETmc8StljQgggAACoRKgBB+qw0FmEEAAAQQQyBgBAnzGOLIWBBBAAAEEQiVAgA/V4SAzCCCAAAIIZIwAAT5jHFkLAggggAACoRIgwIfqcJAZBBBAAAEEMkaAAJ8xjqwFAQQQQACBUAkQ4EN1OMgMAggggAACGSNAgM8YR9aCAAIIIIBAqAQI8KE6HGQGAQQQQACBjBEgwGeMI2tBAAEEEEAgVAIE+FAdDjKDAAIIIIBAxggQ4DPGkbUggAACCCAQKgECfKgOB5lBAAEEEEAgYwQI8BnjyFoQQAABBBAIlQABPlSHg8wggAACCCCQMQIE+IxxZC0IIIAAAgiESoAAH6rDQWYQQAABBBDIGAECfMY4shYEEEAAAQRCJUCAD9XhIDMIIIAAAghkjEAoA/yGDRvs008/tZ07dybZy82bN9u4ceNsz549SeZPnjzZVqxYkWQeTxBAAAEEEMjNAqEL8D169LAmTZrYQw89ZLVr17aFCxe647N8+XK75JJLbP369da0aVM7dOiQm68fAf3797eqVavm5uPIviOAAAIIIJBEIFQB/sMPP7TPPvvMfvrpJ5szZ44NGjTIevbs6TI8ceJE69Chg/Xr18/q169vM2bMcPNHjhxpvXr1ssKFCyfZMZ4ggAACCCCQmwVCFeCnTZtmHTt2tGLFirlj0r17d5s/f75t3LjRypYta/v373fzVUVfokQJU5W9Ar+WIyGAAAIIIIDAUYH8Ryezf2rNmjXWokWLaEaKFy9uRYoUcQG+TZs29uSTT7rXtm/fbuecc44rzd9///1WoECB6HtiJxYtWmQq4QelPHnyBL2c7teeiBxO93ty6ht2v3skp+5auvdr1qxZ6X4Pb0hdgPPtqBHn21ELzrf/WoQqwG/ZssWKFi169Ch5UwrwKrFXrFjRleZVdT98+HAX9FWdP2LECFuwYIFNmDDBbrrpJld976+gcePG9uabb/pP4/6/++67485nJgIIIIAAAoksEKoq+vLlyx/Tc16d6PwOdKq6b9WqleXLl8+GDRtmAwYMsO+++866detmF1xwgbVt29b27t2byMeDvCOAAAIIIJAhAqEK8DVq1LCVK1dGd2zt2rV25MgRq1atWnSeJlavXm3z5s2zzp0725QpU6xdu3YuuJ933nnu8rokC/MEAQQQQACBXCgQqgB/66232vjx413wVkl8yJAh1qVLF8ufP2lLwtChQ23w4MGWN29eV7rfvXu3O3Qq7ZcrVy4XHkZ2GQEEEEAAgaQCoQrwjRo1MnWaa968udWqVcuV1J955pkkOV62bJnp0b59eze/devW7tK6Pn362NatW02leBICCCCAAAK5XSBUAV4HY+DAgbZr1y774YcfbPr06e7yuNiDdPjwYXvttdeis0qXLu0Gw7nhhhvsyy+/tIzuFR/dEBMIIIAAAggkkEDSuu+QZLxgwYKmR7zUoEGDY2ZrWXWyIyGAAAIIIIDAfwVCV4LnwCCAAAIIIIDAiQsQ4E/ckDUggAACCCAQOgECfOgOCRlCAAEEEEDgxAUI8CduyBoQQAABBBAInQABPnSHhAwhgAACCCBw4gIE+BM3ZA0IIIAAAgiEToAAH7pDQoYQQAABBBA4cQEC/IkbsgYEEEAAAQRCJ0CAD90hIUMIIIAAAgicuAAB/sQNWQMCCCCAAAKhEyDAh+6QkCEEEEAAAQROXIAAf+KGrAEBBBBAAIHQCYTyZjNZqaS7z/Xq1SsrN5mubeke9yVLlkzXe3LqwlgcPbJht6hWrdrRzMZMbd++PdTnm+5kWaJEiZgc595JLI4e+7Cfb7rVeryUJ+KleC8wLxwCN910k7355pvhyEw25wKLowcAi6MWGTWlW1HfcccdNmbMmIxaZcKu58iRI9atW7ckt+ZO2J3JgIzffPPN9sYbb2TAmrJ2FVTRZ603W0MAAQQQQCBLBAjwWcLMRhBAAAEEEMhaAQJ81nqzNQQQQAABBLJEgACfJcxsBAEEEEAAgawVIMBnrTdbQwABBBBAIEsECPBZwsxGEEAAAQQQyFoBAnzWerM1BBBAAAEEskQg3xAvZcmW2MhxCdSpU8cqVKhwXO/NaW/C4ugRxeKoRUZN5c2b12rXrs355oFqADAsjn6yEvV8Y6Cbo8eQKQQQQAABBHKMAFX0mXQoP/jgA/Mfn3zyiX399demkbLSk1atWmWjR4+2qVOnpudtxywb5sEKP//8c9N+xqZFixbZpEmTYmfZnj17nOeBAwdM7/nll1+SvJ7dT3799VebMmXKMdnQ0Kz6HKQ1ZdaxOt71arjS5McirfuSlcv555r+c76lLM/5ltTmeM+LpGs59tnxrnfZsmWm778MS15GSJkg4B2gSL169SLnnHNOpEGDBhFvfOvIqaeeGpkzZ06atuaNfRwpXbp05Morr4y88soraXpPvIV++umnSOvWreO9FIp5t956a8QbHjRJXpo0aRLxqksjK1eujM6fOHFipFKlSu75WWedFXn77bejr4VhQvkrW7bsMVnR8dZnwRv685jXks/417/+FRk6dGjy2Sf8/EQ+A0uWLIkUKVLkhPOQ2SvgfEubMOfbUacwnm9/+ctfIrfddtvRTJ7gFCX4DPupdOyKXn75ZZs3b559++23tm3bNvMCrf3P//yPbdmy5diFk83xvpTtjz/+sI8++siNCZ3s5TQ/Ven4xx9/TPPyWb3gFVdcYV988UV0syoJ//zzz3bttdfa5MmTo/NnzpxprVq1cs8XLlxo119/ffS1nDKhfVRNRUansH8GMmp/Od9Sl+R8O2qUG843AvzR452pU/ny5bNnn33WBe133nnHbWv37t32pz/9ySpWrGhVqlSx//3f/7WDBw/a0qVLrX379rZ//34744wzbP78+ZbSslqR3jN48GBTR5BixYpZw4YNXTXlpk2b7Pbbb7f169ebV5uQ7iaCTAX5/yu//PLL3Q8Q/0ePqlcvu+wya9OmTZKq4dgAf91110WDf4cOHdzNQbyaEnfXvXbt2qXpB1RW7Fvybaxdu9YuvfRSe/75561mzZruuHsldrfY66+/bl6thClI9e7d21avXm36Mr7//vtdpy/92An6DGjdstBnyav5sauvvto2btxo8T4DQetRZp544gn3WTrllFNs3LhxyXcjIZ5n5vmWHuuwYZ3I+Rb0+Q3bfio/QflNy/mm7+GLL77Yfa+ceeaZ9uGHH0Z3Mz2fgaDzTXep69Kli3m1f9a8eXN33kc3khETJ1gDwNtTEPCOTWT27NnHvOqVQiPeF7ibr6oY7ws/4pWwI15JP9K0adPIsGHDIocOHYpMnz49UqpUqciOHTvc85SW1Yq8dvqIF9wjXkkt4tUURPr27RupVauWqxZW1XGNGjXceo7JTEhmqAlD+VTq3Llz5MUXX4x4pfhI4cKFI14thst7/vz5I7/99ptbJraKvnHjxpH69etHvLbFiHfSRbwfNxFVc2V1SksV/YoVK1zTg3dnqoiqzb0fMxHt1+LFiyPejzlXNXfPPfdEvFJ8xGuLi3hBKuLdNS7i1eK4fQ/6DHg/iiLej8WI1+bv3uvdPjLy5z//Oe5nIGg9Y8eOjXi3eo3MmDEj4rUFus9kolTRZ9X5lh7rrP4cpmV7x3u+BX1+07LdjFwms883fZd6Vy9FHn744YhX+Ih4PwhcM6tXG+t2Iz2fgaDzzftRHvFqK9333VtvveW+87R8RiXLqBWxnqQCKQV47xaMEe8Xmwtcamf2fhVGtm7d6h7//Oc/XWDWmrxSe+Tkk092K1WQC1p2w4YNEa/E55bVh/Ef//hHpECBAu6510Evuk43I4R/FNT69evnfsio38GaNWtcLk877bSI8q8Ap3Z5PyUP8F7NiP9SZNCgQZEbbrgh+jyrJtL6haPPhddBMJqtunXrRt577z33vEePHi4o64kCvJbVDx2l1D4D+pHgdYhzAV19Fzp16uR+HOi9sZ+B1Nbj1YBEdDz89NJLLyVMG3y8AJ8Z51tarX3DsP0/3vNNAT7o85uV+5nZ55t3i+6IVxsW8WrAot/POjf0o1kprZ+BoPNt79697ns99nOrAmBGBvj83gEjZaGAF7zs3HPPddVHuudyz5493TWnfhZUtaj5sUnVQUHLesHcBg4caNOmTdMPNvNKtMesI3Z9YZtWteHjjz/u+iuUK1fOvBoHl0W1uX/55Zeuatpvf4+Xd1VL+6lo0aLm1YD4T7Psv6rF1eNc/rqG2E+qnitZsmSSebH5VZNKSvnVddleadqtKrXPgKr61KzzzTffOD99XrySmp+N6P/U1rN8+XLzalGiy1900UXR6UScyIzzLa3WYfXKyPMt6PObmfuf2eebmsh0nL1a1SS7oaZUpbR+BoLON+8Hk1vX2Wef7f7rj8439b/KqEQbfEZJpmE969ats7lz59oll1xiCmRK48ePd8FeH4QffvjBvF7Xpi/22JTasnfffbd5tQA2a9Ys0zYU7BVoEiXJQ+1duswsNpBr+quvvnKd8GLnJ9+v2ICq17Jj370rJEyX8Km/Q2zyStOuPTt2XvL8xr4WO63l/GWDPgNeKcH1WVDHRAU0BXl9UcRzCFqPPndqd/dKJ9FsKOAnasqM8y091mF1y+jzLTv2M7PPt/Lly7u+L+rwq+9mPdRhWv1T1BFWfYRO9HzTuaakTth+yujzLWkk8bfC/wwRUMcxdXTSLzJ1HlPnMP1aU296r/rdWrRoYU899ZQr+eka+e7du1v//v2P2XZqy3rt9Oa1RbuSm9bz97//3XWoUymuePHibv3qsBfWpDyqY6DXRJEkwOuLSOMH6NrQsJckNdqg13Rgd911l+soo9K816ZmI0eOdJ3f0mIvB7+zYfLlgz4DCjo6vvLyLsd0VyF4VZjuB4fWE/sZCFqPlm3btq1NmDDBfaGp9mHMmDGanRApK8639FiHFY3z7b9HJuh88y5PNl3Ro8+/fih7/X/cd6zXN8p1lM6I8021H+pYp7FO9u3bZyrRK05kaPIyT8oEAe8gufYq/T/ppJNcRzd1fvOqdqJbU0erCy64IOId6EjVqlUjV111VWTz5s3u9dg2eM0IWlYd8nSNvTrPeL8KI08//bRrN1WHEHW60jyvJBhRW31Y00MPPeT6DXhBJUkWvV6sEe/XcpJ5ydvgvasSoq8/9thjEe9SxOjzrJzwvhAi6nyjTmnqM6Fj6vWCd+3iyoffhhmbJ/Ut8POvvgYFCxaMeD9mop3sYpcN+gyoM5DaDNXp0PsRGXnwwQdd50O9P/lnIGg9Wr5Pnz4Rr1nBXdfvXYWRMG3w/jmX2edbeqzlGcZ0POdbap/frN7PzD7fvNrViFeSj1SuXDlSvXr1iFczGt3F9HwGgs43rwAYadasWcSrWXPnm767MrINnqFqM/Tn0vGtTCVwtaN7gSHVFQQtq1+cqlpKXsWvlf7++++uJJfqBljghAVUi+L9UHOXrKV3ZbrkUe8vVKhQim9N6TOg96ptUJfcxEvJPwMprUfvVUlVfQNUyslpKWi/k+9rSsum1zr5enmecQKZfb7pe1U1dH5zmZ/z9H4GUvosaX1qYvWumjL1wcrIRIDPSE3WhQACCCCAQEgEaIMPyYEgGwgggAACCGSkAAE+IzVZFwIIIIAAAiERIMCH5ECQDQQQQAABBDJSgACfkZqsCwEEEEAAgZAIEOBDciDIBgIIIIAAAhkpQIDPSE3WhQACCCCAQEgE/h/yLuj1PUshxAAAAABJRU5ErkJggg==" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_a03.pdf&quot;, 
       width = 9, height = 3.5)


## get number of candidates with win/defeated/untreated condition
## for each general election

dat_sum_winner_loser_general &lt;- dat_general_describe %&gt;% 
    group_by(election_id) %&gt;% 
    count(winner_loser_before) %&gt;% 
    filter(n &gt; 0)

dat_sum_winner_loser_general$winner_loser_before &lt;- factor(
    dat_sum_winner_loser_general$winner_loser_before, 
    levels = c(&quot;Win&quot;, &quot;Defeat&quot;, &quot;Untreated&quot;)
)

## Figure A04 ----
set.seed(135)
ggplot(dat_sum_winner_loser_general, aes(x = factor(election_id), 
                                         y = n,
                                         colour = winner_loser_before,
                                         shape = winner_loser_before)) +
    geom_point(size = 2) +
    facet_wrap(~winner_loser_before, nrow = 1) +
    coord_flip() +
    ggrepel::geom_text_repel(aes(label = n)) +
    scale_colour_manual(values = c(&quot;darkgreen&quot;, &quot;darkred&quot;, &quot;grey30&quot;)) +
    scale_shape_manual(values = c(16, 1, 17)) +
    theme(legend.position = &quot;none&quot;) +
    labs(x = NULL, y = &quot;Number of observations&quot;)
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_a04.pdf&quot;, 
       width = 9, height = 6)


## get number of candidates with win/defeated/untreated condition
## for each local election

dat_sum_winner_loser_local &lt;- dat_local_describe %&gt;% 
    group_by(election_id) %&gt;% 
    count(winner_loser_before) %&gt;% 
    filter(n &gt; 0)

dat_sum_winner_loser_local$winner_loser_before &lt;- factor(
    dat_sum_winner_loser_local$winner_loser_before, 
    levels = c(&quot;Win&quot;, &quot;Defeat&quot;, &quot;Untreated&quot;)
)

## Figure A05 ----
set.seed(35)
ggplot(dat_sum_winner_loser_local, aes(x = factor(election_id), 
                                       y = n,
                                       colour = winner_loser_before,
                                       shape = winner_loser_before)) +
    geom_point(size = 2) +
    facet_wrap(~winner_loser_before, nrow = 1) +
    coord_flip() +
    ggrepel::geom_text_repel(aes(label = n)) +
    scale_colour_manual(values = c(&quot;darkgreen&quot;, &quot;darkred&quot;, &quot;grey30&quot;)) +
    scale_shape_manual(values = c(16, 1, 17)) +
    scale_y_continuous(breaks = c(seq(0, 800, 200))) +
    theme(legend.position = &quot;none&quot;) +
    labs(x = NULL, y = &quot;Number of observations&quot;)
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_a05.pdf&quot;, 
       width = 9, height = 6)



## Robustness: Stronghold counties ----

## update main regression models by only using 
## &quot;stronghold&quot; counties

## strongholds: general elections
lm_cand_share_general_strongholds_base &lt;- update(
    lm_cand_share_general_base,
    data = filter(dat_general,
                  stronghold_dummy == &quot;Stronghold&quot;))


## strongholds: local elections
lm_cand_share_local_strongholds_base &lt;- update(
    lm_cand_share_local_base,
    data = filter(dat_local,
                  stronghold_dummy == &quot;Stronghold&quot;))


## repeat with more extensive model (adding fixed effects)
lm_cand_share_general_strongholds &lt;- update(
    lm_cand_share_general_all,
    data = filter(dat_general,
                  stronghold_dummy == &quot;Stronghold&quot;))


lm_cand_share_local_strongholds &lt;- update(
    lm_cand_share_local_all,
    data = filter(dat_local,
                  stronghold_dummy == &quot;Stronghold&quot;))


## strongholds: general election, government status of candidate&#39;s party
lm_govstatus_share_general_strongholds_base &lt;- update(
    lm_govstatus_share_general_base,
    data = filter(dat_general,
                  stronghold_dummy == &quot;Stronghold&quot;))


lm_govstatus_share_general_strongholds &lt;- update(
    lm_govstatus_share_general_all,
    data = filter(dat_general,
                  stronghold_dummy == &quot;Stronghold&quot;))


## Figure A10 ----
dat_eff_share_general_strongholds &lt;- ggpredict(
    lm_cand_share_general_strongholds_base,
    terms = c(&quot;winner_loser_before&quot;,
              &quot;incumbent&quot;))

plot_interaction(dat_eff_share_general_strongholds) +
    scale_colour_manual(values = c(&quot;darkred&quot;, &quot;darkgreen&quot;)) 
</code></pre>

<pre><code>## Scale for &#39;colour&#39; is
## already present. Adding
## another scale for &#39;colour&#39;,
## which will replace the
## existing scale.
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_a10.pdf&quot;, 
       width = 9, height = 3.5)


## Figure A11 ----
dat_eff_share_local_strongholds &lt;- ggpredict(
    lm_cand_share_local_strongholds_base,
    terms = c(&quot;winner_loser_before&quot;,
              &quot;incumbent&quot;))


plot_interaction(dat_eff_share_local_strongholds) +
    scale_colour_manual(values = c(&quot;darkred&quot;, &quot;darkgreen&quot;))
</code></pre>

<pre><code>## Scale for &#39;colour&#39; is
## already present. Adding
## another scale for &#39;colour&#39;,
## which will replace the
## existing scale.
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_a11.pdf&quot;, 
       width = 9, height = 3.5)





## Figure A12 ----
dat_effect_govstatus_share_general_strongholds &lt;- ggpredict(lm_govstatus_share_general_strongholds_base,
                                                            terms = c(&quot;winner_loser_before&quot;,
                                                                      &quot;incumbency_status_party&quot;)) 


dat_effect_govstatus_share_general_strongholds$winner_loser_untreated &lt;- factor(dat_effect_govstatus_share_general_strongholds$x,
                                                                                levels = c(&quot;Defeat&quot;, 
                                                                                           &quot;Untreated&quot;,
                                                                                           &quot;Win&quot;))

dat_effect_govstatus_share_general_strongholds$group &lt;- relevel(factor(dat_effect_govstatus_share_general_strongholds$group),
                                                                ref = 2)


ggplot(dat_effect_govstatus_share_general_strongholds, 
       aes(x = winner_loser_untreated,
           y = predicted,
           shape = winner_loser_untreated,
           colour = winner_loser_untreated)) +
    geom_hline(yintercept = 0, colour = &quot;grey30&quot;, size = 1, 
               linetype = &quot;dashed&quot;) +
    facet_grid(~group) +
    geom_linerange(aes(ymin = predicted - 1.96 * std.error,
                       ymax = predicted + 1.96 * std.error),
                   size = 0.5) +
    geom_linerange(aes(ymin = predicted - 1.645 * std.error,
                       ymax = predicted + 1.645 * std.error),
                   size = 1.3) +
    geom_point(size = 4) + 
    scale_colour_manual(values = c(&quot;darkred&quot;, &quot;darkgreen&quot;)) +
    scale_y_continuous(limits = c(-3, 4),
                       breaks = c(seq(-3, 4, 1))) +
    labs(x = NULL, y = &quot;Expected change in vote share\n(percentage points)&quot;) +
    theme(legend.position = &quot;none&quot;)
</code></pre>

<p><img 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fAZsFd1PTq+kV3+LPtVYe6LrgPaL+2720x0nd0vZeaZVdecw5ICvK7LKbDrGrFOJvwq7LBFYtP86nF3fUzX1eOTTjqCdOGFF7rWvHqv4KZrYvFJB6wa/uhV6bLLLnMtf+fPn+9+2HSNTcFRwVXXAxWIdSIQJP3QKKk1v5LyFH8CoXF+FaT707BS/MH/+xhzbQKCYb0qP4nm0zT9WOg6ZvDZGqd08sknu4aFv7/7PS/BcLCu4EQkGM9r5gjsrGPSvyTlvuM60dV3XiewfpVzgaB0fOk3QcekgmyipGNd18GDtMsuu5j+glStWjV3TOqzg6Tb7eKTjklND9qdKNDGJ50M6bjWn05SdJ186NCh5l8CM78Gwf3mfPLJJ6ZtLWjS74eOy/zHtz5LJ/FB0vv4FBx78eOyefh/Etm8lVm+bWqkpRbqOstXoxIFUp0t+1VqKbc8PsgomKuhnhrRKMAr4O9IUgM7v8rM1MAtUYo/cz7wwANdY7b4Hw41tlGDQG2LUvDjprsF1Biti1+aUVIJWg0JlXfdWhb8aX6dxOzMe2VVc6JGeN26dYvlQ40VdZKhH8eCpvj9UNBlmC+6Auk6JrWFOtHV8ag/lajznxiHKeg2M92C6V+X3m42HVMK/mpkGyT1VTF+/Pjgratp03c5vnGbGvTGJ78NjzsJUb5Uolfgjk96r1ozBVv9Pui2N9XK+VXzrlZOQVcBP38KC8b6/dBvnH+ZMraYas789jyuljA2MscHCPBZ8AXwr2+5VthqVapAoQPQb7TmWouGBQ6dqauqK6hCF4V+THRftoKWfhzik0r38S1e46dpWK1fr7zyStd6XdX8mlclDlXJqSpNgV+3Fyldc801pupBLbN8+XJ3r/zf/vY390PiX+N28+ifgrpqInSwx5dCFNT1g6fW7So5qCTiX79zVYSxhXfCwJ///GfXUY1a/6olvN/YyFVDvvzyy65jkYJkQftBFqoGpdq+IGLRnyedx6SqsHV5Sreh5j/pTnVM6phStbVug1NreX1HVYLWeN2Sptvo4qvcJas+NnQbrqrdFYh1AqAW9UHSCbdatKtKXq3S9d3VsadjVMe1Thx0zOuE3m8g5457bYOSPl/HtL7rQY2EWsznv5SmeYPSt+4m0G9EfPIb2LmTBt39o2NfJzA6BpVU0CD9V8APAKSICgQNevK3os+fXb8K27Ug9aurXIMZta5XgxR/F7tGZkEjO7VKjU9qnaqW5/pTAxqlNWvWuMZv+RvKaJoasPi91GkwafIPWtcQSK3OlR/lQY3Q/OvtXnxDNa3AD4KuIZ1a7mpetaj3fzTyrNs/aXHT1DgnPmmb1JhGy/lVda4Vun/LjGucp/nUOMgPnPGLuFb0mjc+KQ/BuKARkRr9BEmt4oPGisE4+fonJsFb10hILYm1HfpM/wfR80+c3PRE6wzGBY2Zvv/+e3cHgayCcbGVMxApgZI4JgXgn2y7u078oJnHoyDHpJbRseLfquruttEyamCbvzW5Gtlp2uWXX+5edVz418hjDUiD763uvPGr4d33XQ3+4hvx+Seonh/k3fI6HtRQNr6BqV/K9vyTFDdex66OFzVq1bqV4hvZ6X27du1cntWwL76Rnab5bXpcozkdN8q38qI7coKkRnbKa3zSXQfxjQTjp2XjcCltlA9EygIBNabRWbSqsAuaVCWuRmtqSKakai9Vtan6TNfzi5K0Xp1Z+63zk17D1vrVX72qs3XNfEeTqg/V45Xucw2r0tvR9RZmfuVD9iqRFyapbYEuMZT0dhQm7yyTWCAdx6TWrMtuuvatviSKklQSVjV6ou+oap384Ot+D1RVr+9hfOM3lbh1HV2/DSrVa13+7Z0Js6PfFR37yY5LhR3VSugedq0zLOk3yT8ZSHrpTceNPk+X7Uh5BWhkl9cjo9+pY5YdTf6Zrztw1F+1GrOpkwpVxxU1uCsfOijr+Y18UiW1wC9sUqO5+OrDwq4nHcvtyLXRRJ+3IydmiZZnXPQEinJMamt054kuo/k1O9t1KFOYrS1oEEzVIFfHXbLgrnzpdyXsuNTJQ9j0+G0LCh/x4+KHOW7iNfIOE+DzeuTsO52Nq5GafpA++OCDnHVgwxGIkoBuidW1cL9zqjy3WhZHHtX6XIE5LCmwU8MUJhStaVTRR2t/lGhu1EBM1YA6iEkIIFDyAsHdMKlK1CWfU3IQRQECfBT3CnlCAAEEEECgiALcJldEQBZHAAEEEEAgigJZXxfr9/ftWn3G924UxR1BnhDIZAE9WUz3JOdPDz30kGstnX887xFAIH0C6qAs/9NAtfasD/Dq1MF/0InrTjR9nKwJAQTiBfRY0URJHSap0yUSAggUj4Dfh4CLcYkCPFX0xWPOWhFAAAEEEChRAQJ8ifLz4QgggAACCBSPAAG+eFxZKwIIIIAAAiUqQIAvUX4+HAEEEEAAgeIRIMAXjytrRQABBBBAoEQFCPAlys+HI4AAAgggUDwCBPjicWWtCCCAAAIIlKgAAb5E+flwBBBAAAEEikeAAF88rqwVAQQQQACBEhUgwJcoPx+OAAIIIIBA8QgQ4IvHlbUigAACCCBQogIE+BLl58MRQAABBBAoHgECfPG4slYEEEAAAQRKVIAAX6L8fDgCCCCAAALFI5AxAX7x4sU2fvz44lFgrQgggAACCGSZQEY8D37z5s127rnnWr169ey1117Lsl3A5iCAAAIIIJB+gYwowd9+++32ww8/pH/rWSMCCCCAAAJZKhD5AP/JJ5/YRx99ZD169MjSXcBmIYAAAgggkH6BSFfRr1y50rp162bDhw+3t99+O+HWd+3a1TZs2JBwmkaWLl3aOnbsmHQ6ExBAAAEEEMhGgUgH+O7du9vVV19tjRs3ThrgBwwYELpf+vbtGzqdiQgggAACCGSjQGSr6IcOHWpjx461unXruhL89OnT3XX4d955Jxv3A9uEAAIIIIBAWgUiW4Jft26dNWzY0IIS+MKFC23NmjXWr18/a926dVoRWBkCCCCAAALZJhDZAN+lSxfTX5Aeeugh+/LLL7lNLgDhFQEEEEAAgRCByFbRh+SZSQgggAACCCCQQiCyJfj8+b7pppvyj+I9AggggAACCCQRoASfBIbRCCCAAAIIZLIAAT6T9x55RwABBBBAIIkAAT4JDKMRQAABBBDIZAECfCbvPfKOAAIIIIBAEgECfBIYRiOAAAIIIJDJAgT4TN575B0BBBBAAIEkAgT4JDCMRgABBBBAIJMFCPCZvPfIOwIIIIAAAkkECPBJYBiNAAIIIIBAJgsQ4DN575F3BBBAAAEEkggQ4JPAMBoBBBBAAIFMFiDAZ/LeI+8IIIAAAggkESDAJ4FhNAIIIIAAApksQIDP5L1H3hFAAAEEEEgiQIBPAsNoBBBAAAEEMlmAAJ/Je4+8I4AAAgggkESAAJ8EhtEIIIAAAghksgABPpP3HnlHAAEEEEAgiQABPgkMoxFAAAEEEMhkAQJ8Ju898o4AAggggEASAQJ8EhhGI4AAAgggkMkCBPhM3nvkHQEEEEAAgSQCBPgkMIxGAAEEEEAgkwUI8Jm898g7AggggAACSQQI8ElgGI0AAggggEAmCxDgM3nvkXcEEEAAAQSSCBDgk8AwGgEEEEAAgUwWIMBn8t4j7wgggAACCCQRIMAngWE0AggggAACmSxAgM/kvUfeEUAAAQQQSCJAgE8Ck4mjZ48caRMGDcrErJNnBBBAAIE0CxDg0wxakqtbNm2azR/7WUlmgc9GAAEEEIiIAAE+IjuCbCCAAAIIIJBOAQJ8OjVZFwIIIIAAAhERIMBHZEeQDQQQQAABBNIpQIBPpybrQgABBBBAICICBPiI7AiygQACCCCAQDoFCPDp1GRdCCCAAAIIRESAAB+RHUE2EEAAAQQQSKcAAT6dmqwLAQQQQACBiAgQ4COyI8gGAggggAAC6RQgwKdTk3UhgAACCCAQEQECfER2BNlAAAEEEEAgnQIE+HRqsi4EEEAAAQQiIkCAj8iOIBsIIIAAAgikU4AAn05N1oUAAggggEBEBAjwEdkRZAMBBBBAAIF0CkQ+wHueZ5999plN8591vm3btnRuO+tCAAEEEEAgawXKRnnLpk6dameffbYddNBBtmDBAqtXr569/vrrVqlSpShnu0Tytm3rVtuyaZNt3bzZ/ZUpV65E8sGHIoAAAghEQyDSJfiePXtajx497L333rOZM2favHnzbOjQodGQi1guZr79tr1/+x329XODbcxjj0Usd2QHAQQQQGBnC0S6BK/SeoUKFZzJJr90unbtWitdOtLnJDt7//F5CCCAAAIIJBSIdICvXLmyy3S/fv1swIABduyxx9r555+fZ0O6detmGzZsyDMu/k2pUqWsY8eO8aMYRgABBBBAIOsFIh3gA32V4ps2bWpjx461cePGWcuWLYNJ9tRTT8WGEw307ds30WjGIYAAAgggkNUCGVHfffnll9ugQYOsQ4cOdtddd2X1DmHjEEAAAQQQSIdAZAP8li1b7IYbbrAlS5bEtrNJkyY2e/bs2HsGEEAAAQQQQCCxQGQDfNmyZW3p0qV277332lb/FrAVK1aYrsWfeeaZibeEsQgggAACCCAQE4hsgFcO77nnHlu2bJk1aNDA6tSpY9WrV7fHuAUstvMYQAABBBBAIJlApBvZ7b///q5jm1WrVplawwet6pNtDOMRQAABBBBA4HeBSAf4YCdVqVIlGOQVAQQQQAABBAogEOkq+gLkn1kQQAABBBBAIIEAAT4BCqMQQAABBBDIdAECfKbvQfKPAAIIIIBAAgECfAIURiGAAAIIIJDpAgT4TN+D5B8BBBBAAIEEAgT4BCiMQgABBBBAINMFCPCZvgfJPwIIIIAAAgkECPAJUBiFAAIIIIBApgsQ4DN9D5J/BBBAAAEEEggQ4BOgMAoBBBBAAIFMFyDAZ/oeJP8IIIAAAggkECDAJ0BhFAIIIIAAApkuQIDP9D1I/hFAAAEEEEggQIBPgMIoBBBAAAEEMl2AAJ/pe5D8I4AAAgggkECAAJ8AhVEIIIAAAghkugABPtP3IPlHAAEEEEAggQABPgEKoxBAAAEEEMh0AQJ8pu9B8o8AAggggEACAQJ8AhRGIYAAAgggkOkCBPhM34PkHwEEEEAAgQQCBPgEKIxCAAEEEEAg0wUI8Jm+B8k/AggggAACCQQI8AlQGIUAAggggECmCxDgM30Pkn8EEEAAAQQSCBDgE6AwCgEEEEAAgUwXIMBn+h4k/wgggAACCCQQIMAnQGEUAggggAACmS5AgM/0PUj+EUAAAQQQSCBAgE+AwigEEEAAAQQyXYAAn+l7kPwjgAACCCCQQIAAnwCFUQgggAACCGS6AAE+0/cg+UcAgUgIjLr3Xlv45ZeRyAuZQEACBHi+BwgggEAaBOZ+Mtp+XbQoDWtiFQikR4AAnx5H1oIAAggggECkBAjwkdodZAYBBBBAAIH0CBDg0+PIWhBAAAEEEIiUAAE+UruDzCCAAAIIIJAeAQJ8ehxZCwIIIIAAApESIMBHaneQGQQQQAABBNIjQIBPjyNrQQABBBBAIFICBPhI7Q4ygwACCCCAQHoECPDpcWQtCCCQwwILv/rK1ixdavNGf2rrV67MYQk2PUoCBPgo7Q3yggACGSkwcdCz9tOMmTbun/+yXxcuzMhtINPZJ0CAz759yhYhgAACCCBAX/R8BxBAAAEEEMhGgYwowU+aNMlmzZqVjf5sEwIIIIAAAsUiULZY1pqmlU6YMME6duxoNWvWtPXr15vnefbqq69agwYN0vQJrAYBBBBAAIHsFIh0Cf7uu++2bt262ZgxY2zixInWokULe/TRR7NzT7BVCCCAAAIIpFGgQCX4d99910aMGGGHHnqoHXbYYVatWjXbf//905iNxKu66qqr7Pjjj49NrF+/vg0bNiz2ngEEEEAAAQQQSCyQMsD37t3bBg0aZA0bNrStW7faHnvsYaeddpqNHDnSjjvuuMRrTdPYNm3axNa0bt06GzBggCvRx0b6A127drUNGzbEj8ozXLp0aVfNn2ckbxBAAAEEEMhygdAA/+uvv9ojjzxis2fPti+++MJVlXfo0MFW+h05vPjii8Ue4AN7Bff27dtbvXr1rGfPnsFo96qgH5b69u0bNplpCCCAAAIIZKVA6DX4RYsWWaNGjUxV4/GpcePGNmXKlPhRxTb8888/W8uWLa1ChQr21ltvWdmyoeckxZYPVowAAggggEAmCYQGeFXLz58/30aNGhXbJrVmf/zxx61p06axccU18OOPP9rJJ59sBx10kLv2XrFixeL6KNaLAAIIIIBAVgmEFocVUPv372+tW7e2qlWrWrly5Wzw4MFWvXp1+/zzz4sdokuXLq5a/oEHHrDly5e7zytTpoz7/GL/cD4AAQQQQACBDBYIDfDaLgX1yZMn26effuqCrFrPt23b1oq7ND116lTXkE95qF27tl5cqlu3rqtVCN7zigACCCCAAALbC4QG+Llz57oW6KtWrdqu9fr2q0rvGF0CUMc2JAQQQAABBBDYcYHQa/B16tRx96HrNjk1diMhgAACCCCAQGYIhAZ4NahTI7vrrrvOdW6jFuzBn7qQJSGAAAIIIIBANAVCq+grV65sH374YcKc77777gnHMxIBBBBAAAEESl4gNMCrFzjdB09CAAEEEEAAgcwSCA3w2pTNmzfb2LFjTQ3tlNRdrarut23bZp07d3bj+IcAAggggAAC0RJIGeBPPfVU++qrr6xSpUq2yy67mLqv3bJli9EFbLR2JLlBAAEEEEAgXiA0wC9evNj0TPYffvjB+vXrZ7vttpv16NHDLrnkEveM9vgVMYwAAggggAAC0REIbUX/yy+/uEfE7rnnntasWTP77LPPXCv6W2+91QYOHBidrSAnCCCAAAIIIJBHIDTAq9c6PUlOJXl1PKNr8bomv2bNGtu4cWOeFfEGAQQQQAABBKIjEFpFv+uuu7p74I844gj76aef3ENfNLxkyRK7//77o7MV5AQBBBBAAAEE8giEBnjN2adPH9NDX5SGDx9uzz77rB188MHWqlUrN45/CCCAAAIIIBA9gdAqemVXVfK6Fj906FB75513XI92erKbrseTEEAgscA3C6bZTc/8X+KJjEUAAQR2gkDKEny7du3cU92aNGli5cuXj2VJJfjjjz8+9p4BBBD4n8DPa1bYmBmj/zeCIQQQQGAnC4QG+EWLFtlHH31keq1Zs+ZOzhofhwACCCCAAAKFFQitotfjWvVEOYJ7YXlZDgEEEEAAgZIRCA3w++23n6lq/oknnnDX4Usmi3wqAggggAACCOyoQMIA36lTJytVqpT7e/31192tcnvttVdsnKZdcMEFO/pZzI8AAggggAACO0kg4TX4Rx55xO68887QLKjbWhICCCCAAAIIRFMgYQm+Ro0adsABB7g/PUXujTfecMPvvvuuHXfccXbNNde4B89Ec5PIFQIIIIAAAggkDPABi54ad84559imTZvc42JvvPFGu/vuu03V9XfddVcwG68IIIAAAgggEDGB0AA/d+5c17GNHi6j0vu+++5rV199tfXs2dM9ZS5i20J2EEAAAQQQQOC/AqEBvnTp0rZ27Vo3q7qpVac3SnPmzLFGjRq5Yf4hgAACCCCAQPQEEjayC7LZsGFD0/X4ww47zL777jv74osv7LXXXnOl+JdffjmYjVcEEEAAAQQQiJhAaIBXXt9//30bMWKEHXLIIe4hM7oe/+WXX7ony0VsW8gOApEQ2Lhpo42bNdZWrVtlE78bb80POCoS+SITCCCQWwIpA7xuh+vQoUNMpXnz5rFhBhBAYHsB9UN/7yu/32b6jzcftsG9hmw/E2MQQACBYhZIGeAL8/l6At2QIUNs4sSJNn36dFuxYoWr5tez5HWbnV5J6RPY6nvP++R/DzZZMG6c/ea3nahAXwXpQ2ZNCCCAQIYJhDayK8y2DBs2zAXzQYMGWaVKlVzDvF69epm6vdUjZlUbcO6559q3335bmNWzTD6BX/0HAfVtfqSN/cfjsSkzhr9pDzc6yJZMmhQbxwACCCCAQG4JpLUEf8UVV7gH03z66afu9rpElNu2bTN1f3vZZZdZ79697ayzzko0G+MKICDL59qfZ8umfbPd3Gt+WGrPtG5jN86aaRUrV95uOiMQQAABBLJboEABXvfAq6HdoYce6krn1apVs/333387GT2UZpdddtlufPwI3Xp33nnnub/gFrz46QwXXGDOe+/ZkgkTky6wZukyGz9woP3h//4v6TxMQAABBBDIToGUVfQqZV955ZU2depUmzx5si1cuNCaNWtmn3/++XYiqYJ7/gXozz6/yI69X/TVVykXWPTV+JTzMAMCCCCAQPYJhAb4X3/91fTgGV077969u9t6XUPXuBdffDFUQw3s/v73v7t5+vbta9WrV7c//vGPtnr16tDlmFhwgVJ+bUiqVJB5Uq2D6QgggAACmScQGiEW+Q241GNd/fr182xZ48aNbcqUKXnGxb+hD/t4jeIbrn/CCSlXXv+E41POwwwIIIAAAtknEBrg1ZPd/PnzbdSoUbEtX79+vT3++OPWtGnT2Lj8A/Rhn1+keN7v37KlNTj5pKQr37NBfWveuXPS6UxAAAEEEMhegdBGdhUrVrT+/ftb69atrWrVqlauXDkbPHiwq25PdA0+YKIP+0Ci+F8vHTrEtaSfP2Zsng/b+6BG1vmN4VbOv1WRhAACCCCQewKhAV4cl1xyieuc5oMPPrDly5e71vNt27Y1Bf9kiT7sk8mkf/yu/h0N3Ud/Yh/4j/Eddfc97gMOv+Riu+CZgVbGPyEjIYBA8Qts8x+tTUIgagKhAV79zqt6/swzz7Ru3brF8v7999+7FvXB0+ViE/47oOX07PitW7fm6cNeD6pZs2ZN/tl5X0SBUqVKWW3/zoYg1Wx6KME9wOAVgWIU2Lxxo43sfZtNeGZQ7FOePbedtf1nXzu4TZvYOAYQKAmBhAH+559/dtfe1Yr++uuvd1XyQeY8z3Pd0M6YMSP2+NhgWvxyt9xyi7300kuma/ZqUa/l9OCaRMsFy/OKAAIIZIrANr8AM+jsc+y7D//XRkl5/3XBAht0zrl20QvP2+EXXZQpm0M+s1AgYYBXMG7fvr2tWrXKPQ++VatWsU3X9XV1dHPffffFxgUDhV0uWJ5XBBBAIFMEvvbbI+UP7rG8e2bDr7nWGp9zjpXfddfYaAYQ2JkCCQO8AvgC/yxU96xfd911pn7lC5IKu1xB1s08CCCAQJQEpr36Wmh2Nvyy0r776CNrfPbZofMxEYHiEkgY4IMPq+z3Ya7grqp2dVe7bt0611XthRdeGPo8+GC5YD28IoAAAtkmsGbZspSbVJB5Uq6EGRAopEBogNc6b7jhBhvo92euXuiaNGni+qT/17/+5RrfqW/6ZEkPQhk7dqxrea/hIO2777527LHHBm95RQABBDJSYI969WzJxK9D875nvk7CQmdmIgJpFggN8Gpkp25mZ86caQcccID76Hvuucduvvlm69evnz355JNJs6MTAt0rX88/CMqXLx+b77TTTiPAxzQYQACBTBVo/qdO9s1rw5Jmv2rd/az+iScmnc4EBIpbIDTA//TTT6Z72oPgHmTmbP+aklrJJ0vz5s2z8ePHm17VBz0JAQQQyDYBNaA76orLbfx/Bm63aWUrVbQLnx1kZeMKN9vNxAgEilkgtKvaAw880N3e9vHHH8eyoX7m1budquuTpQoVKpiuw++9997JZmE8AgggkPEC5w14ytr1f9Iq+T19Bqn+SSfZNV9+YQ38VxICJSkQGuCVMVXHn3766aYHzKgHO7WUHz16tN1xxx1J8127dm1r0aKFq8LXrXYkBBBAIBsF1MnUMVddZYdd1DG2eec8/pjVDGmfFJuRAQSKWSBlgO/SpYvrqEY92SnIP/roozZr1ixTEE+WdO1+zJgxds0117g+7HUQBH8XXHBBssUYjwACCCCAAAJpEgi9Bq8uZxcvXuyeHBf29Lj8eVH1vAJ8orTbbrslGs04BBBAAAEEEEijQGiA37Bhg7vWrur2yy67zM4//3zbNUWvTOrNTr3d7b///mnMprkTBp1kVKlSJa3rZWUIIIAAAghko0BoFb2C6dKlS+3iiy+2p59+2mrWrGlXXHGFu789EUanTp2sQ4cO9ssvv7ggr0Cf/0+d5Oxo+vDDD62l/+zzhQsX7uiizI8AAggggEBOCoSW4CWi58Dr+rv+9BS5Z555xnQvux4jq6Afn/7+97+7VvdaZu7cufGTYsOpagBiM/oD6jnvzjvvtBdeeMGtN34awwgggAACCCCQXCBlgNeiujXuvffec4H2nXfecaXpixI8JSn+nvcGDRok7OJW4wuaJk2a5HrC+/rrr21Hlivo+pkPAQQQQACBbBUIDfC//fabe677K6+84kryug7/8MMPh7agD6AK28VtsLxeTzjhBPcXPy7/cNeuXU1tBZIlXSLo2PF/t7Akm4/xCCCAAAIIZJNAaIDfuHGje577sGHDUgbaeJSidHEbv56CDA8YMCB0NnW1S0IAAQQQQCDXBFI2svvPf/6zQ8FdgGFd3E6dOjXXjNleBBBAAAEEdrpAaIAvbG4K28VtYT+P5RBAAAEEEEAgr0BoFX3eWXfsXdDFrR5Wo4D/ySef2O67725ffPHFjq2IuRFAAAEEEEBghwVSBvjCPtddXdweccQR9tFHH7kq+3P8Jy/pHvgduU1uh7eGBRBAAAEEEEDACaQM8EV5rrvum9ffihUrbI899jA9eKawAT6spTz7EgEEEEAAAQTyCoQG+KI8171Xr1727LPPWqtWrWy//fazl19+2dSifeTIkaGPms2bPd4hgAACCCCAQGEEQgN8YZ/rvnr1ahs4cKDNnj3batSoEcvX7bffbo8//rg99dRTsXEMIIAAAggggED6BUJb0Rf2ue7Lly+3OnXq5Anuyvopp5yStAvb9G8aa0QAAQQQQCB3BUIDfGGf664nyenBNOpHfv78+aaGeuPHj7e77rrLPZUud7nZcgQQQAABBHaOQGgVfWGf675y5UpT//EffPCB9enTx3bZZRfXI542Sc+J11PnlD7++GM7+eST3TD/EEAAAQQQQCB9AkkDfFGe667HzBbkfnddAiAhgAACCCCAQPoFEgZ4lbB1W5oaw+21114JP1XPfR8yZEjCaXrAywEHHJBwGiMRQAABBBBAoPgFEgb4dD/Xvfg3g09AAAEEEEAAgXiBhAE+/3Pd4xdgGAEEEEAAAQSiLxDaij762SeHCCCAAAIIIJBIIGEJPtGMhRn37rvv2ogRI+zQQw+1ww47zKpVq2a6hY6EAAIIIIAAAsUrUGwl+N69e9uVV15pev775MmTbeHChdasWTP7/PPPi3eLWDsCCCCAAAIIWIFK8DtaElcHOY888ojrqla3y+ned7W61/3xL774oh133HHQI4AAAggggEAxCqQswRemJL5o0SJr1KiR1a9fP0/WGzdubFOmTMkzjjcIIIAAAgggkH6B0BJ8YUviDRs2dF3Ujho1Kpbj9evXuwfNNG3aNDaOAQQQQAABBBAoHoHQAB9fEo/vmU4l8cGDByfNUcWKFa1///7WunVrq1q1qpUrV87Nr9vvuAaflI0JCCCAAAIIpE0gNMAXpSR+ySWXuGvt6o9eT5dT6/m2bduagj8JAQQQQAABBIpXIDTAF7YkvmnTplg3tnrQTN26dW3Lli322muv2a677mo6cWjSpEnxbhlrRwABBBBAIIcFQgO8XApTEv/tt99M3d3q9rgaNWrYvvvua5MmTbKyZctagwYN3C1z1157rT3wwAM5TM+mI4AAAgggUHwCoa3oVRJ//vnn7bPPPnOPfI0viQ8fPtymT5+eMGcqta9du9aGDRtmS5cutQkTJthPP/1kzZs3t4cfftjmzJnjbpebO3duwuUZiQACCCCAQGEFRk35wPSX6ym0BF/YkrgCuErr7dq1i/nqqXSXXXaZvfHGG9amTRtr1aqVTZw4kZ7tYkIMIIAAAgikQ+CTab/fwXXqYaelY3UZu47QEnxhS+Kqhl+8eLGrog9ktm7d6q7B16lTx/Ss+VmzZtnBBx8cTOYVAQQQQAABBNIoEBrg40vipUqVch8bXxKvXbt2rCQen6cKFSrY3/72Nzv66KPdn1rPazlV1+va+9VXX21lypRxfdTHL8cwAggggAACCKRHIDTAF6Ukfs0117hr75deeqkdeeSR9sknn7he7PbYYw+7+eabXfe16dkE1oIAAggggAAC+QVCr8HHl8QPP/xwq1WrlgvUamyXqiS+efNm+/nnn22//fZzn6kGddOmTbNt27ZZ586d8+eD9whkpcCGTRuycrvYKAQQiL5AaIBX9lUSP/HEE11gX716td11112mYK+kknj+/ubdBP/fqaeeal999ZVVqlTJtcBXt7e6F75v377BLLwikJUCP/yyJLZd0xdMiw0zgAACCOxMgZQBXplR//GJ+pBPFtzVwE63xv3www/Wr18/22233axHjx7unvqaNWvuzO3jsxDY6QIPv3Z/7DMXrVho47/90o5q2CI2jgEEEEBgZwiEXoNXBlSl/umnn7oW8EOHDrXgb9y4cUnz98svv7gGdHvuuad7Brzuo9dtc7feeqsNHDgw6XJMQCDTBT6fOdbenfhWns34y8Dr3Z0jeUbyBgEEEChmgZQl+D/+8Y/uATH16tWz8uXLx7Jz2mmn2bHHHht7Hz+gfudnz57tbpVTyX/s2LGma/Jr1qyxjRs3xs/KMAJZI6CT4b88c/122zPhu6/s5U+ft4tO6rTdNEYggAACxSUQGuDnzZtn48ePN73qSXAFTepv/rrrrrMjjjjC9WB30EEHueElS5bY/ff/r/qyoOtjPgQyQWDwx8/YlO8nJczqX1+4xc5p0d52rbhrwumMRAABBNItEFpFr1b0lStXtr333nuHP7dPnz4WPGJW3dp269bNPYCme/fuO7wuFkAg6gKr16+2u1+6LWk2l61cag8P4+Q2KRATEEAg7QKhAV4d2bRo0cKefPJJW7VqVYE/XH3Yjxgxwj1YRgvpJEG31anq/vXXXy/wepgRgUwReODVe+ynVT+FZveJtx+1+T9+HzoPExFAoGgCazestVXrV7k/DedyCq2i161tY8aMcQ3rdLtcfOrQoUPskbDBeN33Pn/+fNNy119/fZ5qfXVPO2TIEJsxY0aePuqDZXlFIFMFvlv6rfUbkfr2z982/2a3Df6LvXDjq5m6qeQbgcgLvPb5Kzbwg3+7fDbf/0jrfOoVkc9zcWUwNMCr5K0Anyjp1rf8SUG8ffv2rrSvp8npgTJBKl26tFWrVs3uu+++YBSvCGSFgErvu1bwr61X+H1ztvnHwWq/BKFUrky5PNfdR3/zkX015ws7+sBjfp+Z/wgggEAxCYQGeAVl9VqnVvBBFb0eGrN+/fqEPdIpgC9YsMDUIY4a2Q0aNKiYss1qEYiOwNPXPpcnM0t+XmyNuv/eg2Obo8+1wb2G5JnOGwQQQGBnCIQGeGWgMD3SqeSv+911//zy5cvdyUCwMfvuu2/S2+uCeXhFAAEEMkng5JtvskXjJ9gh7dvZ3gcemElZJ69ZLBAa4IvSI11h7p/PYmc2DQEEslhgD7+ms2KVylat4QFWzu+em4RAFARCA3z+HulU5a7Gc+qRTn3Sn3POOQm3obD3zydcGSMRQAABBBBAYIcFQm+TK2yPdEW5f36Ht4AFEEAAAQQQQGA7gdAAH98jna6dBz3SnXvuuda2bdvtVhaMKOz988HyvCKAAAIIIIBA0QRCA7xWXZge6YL753XvfNWqVa1UqVKxvwsuuKBoOWZpBBBAAAEEEEgpEHoNXkvrITG6Fj9x4kT3PHfdCqeW8XpC3PHHH5/wA3b0/vmEK2EkAggggAACCBRaIGWAb9eunY0cOdKaNGmS52ly6sQmWYDX/fMHHHCAvfvuu67L2kMPPdQOO+ww19HNPvvsU+jMsiACCCCAAAIIFEwgNMAvWrTIPvroI9NrzZo1C7bG/87Vu3dv19FNw4YNTZ3j7LHHHqZHzOpk4bjjjtuhdTEzAggggAACCOyYQGiAV9ezderU2eHgrmvwjzzyiHsmvJ4op+5u1Xf9ypUr7cUXXyTA79g+Ym4EEEAAAQR2WCC0kd1+++3nquafeOIJdx2+oGtXib9Ro0ZWv379PIs0btzYpkyZkmccbxBAAIFsEDji0kus+sEHZ8OmsA1ZIpAwwHfq1CnW6l2Pd1W/8nvttVdsnFrFh7WGV7W8nio3atSoGJP6r3/88cetadOmsXEMIIAAAtkicGSXLraP31aJhEBUBBJW0at6/c4774zlUVXuut1NSYG7Xr16luhpcsECFStWtP79+1vr1q3dcuXKlbPBgwe7x8d+/vnnwWwFfh0/frybt1mzZla2bMIsF3hdzIgAAggggEAuCCSMljVq1DD9KT311FOua9ply5aZAvUNN9xgP/30k3tGfBjQJZdc4q61f/DBB+62OvWKp85xFPwLmvR8+WOOOcZ0292PP/5oqhl4++23TR3wkBBAAAEEEEAguUDCKvpgdrV+V0c3o0ePdsFd44cNG2YnnHCCe1pcMF+y1ypVqli3bt3stttus+bNm+9QcNc6e/bsaWeccYa7B/+7775zlwiefPLJZB/HeAQQQAABBBD4r0DCEnyg8+2339qee+5phxxySDDKypQpY7o3Xg+cueOOO2Lj8w/kL/nfeOONsZK/ur0tSNItejqhUFLJX7UCzzzzjP3lL38pyOJunrVr17rLA6oFSJa03l122SXZZJszZ447yUk6gz/h/PPPd7cCJptn4cKF9t577yWb7MafffbZFtZPgGox3nzzzYTrWDt5csLx+UfqcsvQoUPzj87z/sQTT3SNJPOMjHuzYcMGe/755+PGbD/YokWL0PYW27Zts//85z/bLxg3RpdkjjzyyLgx2w8+++yztmnTpu0n/HfMwX6jJ52QhqWXX37Z1qxZk3SWBg0auMcmJ53Bn6C2KitWrHCz/LpxZWzWefO+twEDBlitWrXcJavYhAQD6jdiyZIlCab8PkrtYNq3b590uibomJk7d27SeXRp7aKLLko6XRPUidWMGTOSzqOavC7+9eaCJNX2afuTpXRsk2r0Lr744mQf4can2iZd+rvssstC1zFhwgSbNGlS6DyXX365+41MNtM333xj48aNSzbZjU/1e6Tf5U8++SR0Hen4PWrTpk3oHVRhv0dB5nRrtC7pJkurVq2yIUOGJJvsxqf6Pdq4caP7jQ9W8sXiscGge2T5pnnbLNXvkRYI+55quvpyOfroozWYNKX6PVJ373/4wx+SLq8JqX6P5CnXgqTQAK8qcQUm3buux78qCVPX1/XDmSwlK/nfcsstruQfdmIQrFM/2j/88IP7YQzG6V58XSqIT127djUFnGRJP9xapnz58slmsfPOOy80wOsH87nnnku6vCYIXPf6J0u6syDVOvQlDAvw+rFMto49fv7Fku+R/+VKB1SydQRz6VkCugsiWdJ3INU6dt9999AAr1swU61D86QK8LrtUg04kyU9NyFVgH/ttde2+17Fr69ly5YpA/w777zjbgvVcptK/2ZW/fc1LFgw356b8pyrwVKblLCky1kKIsmSjsdUAV61bQryyVL16tVTBvgvv/zSnbAkW0cl/3GoBQ3wOukJ28+6dJdqm3Sb7YcffpgsO65tT6oAn2qbVIBIFeB1B1DYtiiDnTt3Dg3wM2fOTLkOFaDCChwF+T069dRTQ3+PdCKZaluOOuqo0ACfat/KQ78jqQJ8qnzodz/s9+i3337Lsy0rKv1oVkWfbva5fzI1Z8M8d1k3rIF3QX6PdOKVKsArOKtQmSypEJcqwKuwoNiXLJ100knpCfAqrQ8cONCV2FWS18GoBm8qgeugS5aKUvIP1qnr70KPb8ynL31+vFRnXffee687AYhfT/AZwauefheWtL1nnXVW2Cx58ploRrVpSLWOsBMErVPTk61j84yZtm7mrEQfnWecSjvJ1hHMmKqGRV6p1qFSb1jSnRip1hF2UAfr1iUcHeDJUnztU7J5TjnlFFPNRrIUdjIbLKNeHXV8KK3etMqmf/N7oK65T00767izQn/kgnXoBE8BOFkK2sUkm67xRxxxROilMF02S5XUa2WYadjJcv516/O0XclSQbZJNTlhnxlWOxd8rr4HYdukWolUSSdYqb6z6sUzLOnW4VTrSNVOqSC/RzrBDkt77713ynzoNz8shf0eBcuFFVg0j36XU3moL5awpH0Xv47JKybYooXfuUWa+r2oHl7tSEvH75FK36nS6aef7grByeZTr66pkgoU6jMmWSrI72JsWT+IpkwLFizwXnnlFc9vXe/5Veaef6CELrNlyxbPP7C9ESNGxObzS9mef/udd9VVV8XGhQ34feB7fhDwvv/++9hs/pmN51/Lj70vyIB/a57nVyUVZNaMnueb4cO9m6y0+/v4wQczelsyPfOLVyzydj2/lPu79O8dMn1zCpT/P//5zwnnSzY+4cyMRCANAoM+fDp2/Gk425Pird/OLeFmhlbRB2cB6vBGfwVNhS35x69f18RUVazqqKCKJ344fl6GEUAAAQQQQCCvQIECfN5FCvbOP6uwqVOnukYpuv6sFvG6BhlW1ZZ/zbqe9dBDD7nGDXqinarjH3zwwfyz8R4BBBBAAAEE8gkUS4BXSbtjx46mBl1hPd7ly8t2b9XyXsvryXQ6MVDDHjWaIiGAAAIIIIBAuECxBHg1ilCjo0GDBrnbV3QrTGGSes97//33TQ3u1GhkR0r/hfk8lkEAAQQQQCBbBBIGeN0OEnY/rjZety6odWuipNuW1KWt+rDXn67JB0n3Z+pWgh1JhT1B2JHPYF4EEEAAAQSySSBhgH/hhRdiHaqsXr3ali5d6u7L1G1A06dPN90KonvakwV43baS7L7VVLdvZBMu24IAAggggEBJCSQM8GrYpj+/3b0L4g888IC7pq4qcjV28291c0+XS5ZpnQDs0L16yVbEeAQQQAABBBAolEBorwzqolW9lv3pT3+KXf9W5wfXXnutDR8+POUHqutNzatua9WTlBrfkRBAAAEEEECg+AVCA3zdunVt8eLF9vXXX8dyotvf1Hju8MMPj41LNNC7d2+78sor3a1yk/1+0tXlrar0C/O42ETrZxwCCCCAAAIIJBdIWEUfzK4uE3XfubqaVDd96mbx008/dZ3eqO/tZEndfuqZ8rNnz7YvvvjCdWvboUMH1/2e+g4/7rjjki3KeAQQQAABBBBIg0BoCV7r79Gjh6kErlc98lWld5XCw/oHVsc2ugavE4L41LhxY1MLfRICCCCAAAIIFK9AaAk++Gg9fEJ/BU16KINukxs1alRsEd065/cLH/qEsdjMDCCAAAIIIIBAkQQKFODVWM5/cIzpSTh6Jm61atViT85K9Omq2tcjZdU1rTqr0dN+Bg8e7J6UxTX4RGKMQwABBBBAIL0CKQO8GsupWl6lcj3nXY8I1LPP9Yz4sGvpenaupusZ18uXL3cnBG3btg19lGV6N421IYAAAgggkLsCoQG+qI3l9LAZdYyzYsUKd5ud7qGvVatW7moX45aX9p++V9qvKbFt26yMXkkIIIAAAjktENrIriiN5Xr16mWXX365LVu2zAV1dU+r1vgK+KT0CxzsXw754333WPMune0P/pP7SAgggAACuS0QWoIvbGM5dW87cOBAd5tcjRo1YsK33367a2injm9ICCCAAAIIIFB8AqEBvrCN5XTNXbfRxQd3bcIpp5xi9913X/FtDWtGAAEEEEAAAScQGuA3bdpk6pp25syZeRrLqRc7tYZv165dQkY9lEZPm7vzzjvtsssucx3jTJw40e666y7r1q1bwmUYiQACCCCAAALpE0gY4PX8dd3HrkZ2119/vb300kuukxt9rB5Ao+r3GTNmJA3wK1eudN3bqgV9nz593JPodB+80pgxY6xTp05u+OOPP7aTTz7ZDfMPAQQQQAABBNInkDDAK4i3b9/eVq1aZWvXrrVWrVrFPlFPitN98GFV7VWqVHFd1MYWSjJQu3btJFMYjQACCCCAAAJFEUgY4BXAFyxYYGosd91117n74AvyIX/961/dY2XV690BBxyQdJF169bZgAEDrEGDBnbOOecknY8JCCCAAAIIIFA4gdDb5CpXruyCu+5fD9K3334bDG732rlzZ1cl36ZNG3v22WddNf6aNWtMT6DTcm+99Zap4xydAGzYsMFOP/307dbBCAQQQAABBBAoukBogNfqdUubbpfbvHmz+7Qbb7zRjj32WPcY2fwfr1L7K6+84oL4kCFDXKt5nSRUqFDBdXF7//3322+//Wbjxo2zW2+9lV7t8gPyHgEEEEA+myEvAAAbYUlEQVQAgTQJJKyiD9atrmnVSG706NGuP3mNHzZsmN1yyy2uod0dd9wRzJrnVV3UBo+TVS92+tNJQpkyZfLMxxsEEEAAAQQQKB6B0BK8qtV1m9whhxwS+3QFad0eF/+kuNjEBAO6nq9nyRPcE+AwCgEEEEAAgWISCA3wKnUvXLjQPVgm+PyNGze6J8UdfPDBwSheEUAAAQQQQCBiAqFV9Cp16553ldhVklcHNuPHj7d9993X3c8esW0hOwgggAACCCDwX4HQAK95dD/87Nmz3X3tevhMT/9BJnrOe/ny5UFEAAEEEEAAgYgKhFbRB3n+5ptvXIl99913d0+GU6AnIYAAAggggEB0BVIGeN23fuWVV5qe7T558mR3Tb5Zs2auL/robhY5QwABBBBAILcFQqvo1Rf9I488EquiVz/yHTp0MPU1/+KLL5puhyMhgAACCCCAQPQEQkvwqopv1KiR1a9fP0/OGzdubFOmTMkzjjcIIIAAAgggEB2B0BK8bpPTU+Xi73nXU+Eef/xxa9q0aXS2gpw4gTotWtiefv/+JAQQQAABBEIDfMWKFd0972o1X7VqVdeb3eDBg6169epcg4/gd6fBiSdGMFdkCQEEEECgJARCA7wydMkll7hr7Xq2+/Lly9298G3btqUf+ZLYW3wmAggggAACBRRIGeC1HrWgnz59uutTXve/6+lytWrVKuBHMBsCCCCAAAII7GyB0EZ2ykyvXr3s8ssvt2XLlrmg/vLLL1sL/1qvAj4JAQQQQACBKAl8+8OcWHbih2Mjc2ggNMCvXr3adVU7Y8YM9xjYhx9+2CZOnGh67rsa2pEQQAABBBCIisC2bdvs1c9ejmVHwxqXqyk0wOuae506daxGjRp5fE455RSbO3dunnG8QQABBBBAoCQFBo162hb//L+eVjWscbmaQgO8Hi5Ts2ZNu/POO93tcjoT0sNm7rrrLrvsssty1YztRiBUoJSVik2PH46NZAABBNIusGrdKuvz8u3brVfjNC0XU2iAV491X3/9tfXp08d1dqO+6I8++mjXL32nTp2sVCn/58v/++STT3LRjm1GIKFArb1q2zt3jrJD6x1mz/V6JeE8jEQAgfQKPPBqH1uxesV2K9U4TcvFFNqKvkqVKu4pcqlgateunWoWpiOAAAIIIFAsAnOWzLZ+I55Ium5Nu/y0q6xhrQOTzpONE0IDvOd5VrduXdfBTf6NVze2uj5PQgABBBBAoCQFbnm2l23ZuiVpFjTt5kE9bVjvd5LOk40TQqvoV61aZX/4wx/s+++/j237hg0brEePHnbjjTfGxjGAAAIIIIBASQi8P2mE6S9V0jwfTBqZarasmh4a4FVFrwDfvHlzGzJkiLtF7ogjjrBp06bZvffem1UQbAwCCCCAQGYJbN6y2W4Z1KvAmVYpXsvkSgqtoi9Tpozp3vczzjjD1D3txo0bXYO7W2+91TWuyxUkthMBBBBAIHoCuoz8+m15S++vffaK3fHirS6zfS7+m513/IV5Mq5lciWFBngh6GlyDz74oO2zzz6mx8QOGDDAjjnmGNO98CQEEEAAAQRKSqB8ufJWt3q9PB+/V+Vqsfcazj89NjEHBkKr6NesWWOHHHKIa2g3efJke/PNN+2OO+6wdu3auaCfAz5sIgIIIIAAAhkpEBrgtUV6POzTTz9tu+22m9tAdXAzadKkhC3rM1KATCOAAAIIIJCFAgkD/H333WcvvfSSqWMbldZHjhxpM2fOjG3+iBEjbMKECbH3DCCAAAIIIIBAtAQSBvhZs2bZ4sWLYznt37+/670uGLF161bbsiX5PYfBfOl8HTNmjOm2PRICCCCAAAIIpBZIGOBTL7Zz5/jwww+tZcuWtnDhwp37wXwaAggggAACGSoQ6QC/bt0616GO+r3PpVsbMvS7RLbjBA6qfbD99cI+cWMYRAABBHauQKQDvBrz6ZG1euBN+fLld64Mn4ZAEQRq7LGPnXXk2UVYA4sigAACRRNIeh/8d999Zx999JFbu4KsrssH7+fMmVO0Ty3g0ieccILpLyx17drV1H1uslS6dGnr2LFjssmMRwABBBBAICsFEgb4cuXK2bPPPuv+gq2eOHGiPfnkk8FbO++882LDJTmgjnfCUt++fcMmMw0BBBBAAIGsFEhYRT9w4EDXLa26pk3298ILL6QVRA+wUWlbf9WrV0/rulkZAggggAACuSaQsARfEgi33Xabde/e3X102bKRyVZJUPCZCCCAAAIIFFkgMpG0Vq1apj8SAggggAACCBRdIGEVfdFXyxoQQAABBBBAoCQFIlOCT4UQ1lI+1bJMRwABBBBAINcEKMHn2h5nexFAAAEEckKAAJ8Tu5mNRAABBBDINQECfK7tcbYXAQQQQCAnBAjwObGb2UgEEEAAgVwTIMDn2h5nexFAAAEEckKAAJ8Tu5mNRAABBBDINQECfK7tcbYXAQQQQCAnBAjwObGb2UgEEEAAgVwTIMDn2h5nexFAAAEEckKAAJ8Tu5mNRAABBBDINQECfK7tcbYXAQQQQCAnBAjwObGb2UgEEEAAgVwTIMDn2h5nexFAAAEEckKAAJ8Tu5mNRAABBBDINQECfK7tcbYXAQQQQCAnBAjwObGb2UgEEEAAgVwTIMDn2h5nexFAAAEEckKAAJ8Tu5mNRAABBBDINQECfK7tcbYXAQQQQCAnBAjwObGb2UgEEEAAgVwTIMDn2h5nexFAAAEEckKAAJ8Tu5mNRAABBBDINQECfK7tcbYXAQQQQCAnBAjwObGb2UgEEEAAgVwTIMDn2h5nexFAAAEEckKAAJ8Tu5mNRAABBBDINQECfK7tcbYXAQQQQCAnBAjwObGb2UgEEEAAgVwTIMDn2h5nexFAAAEEckKAAJ8Tu5mNRAABBBDINQECfK7tcbYXAQQQQCAnBAjwObGb2UgEEEAAgVwTIMDn2h5nexFAAAEEckKAAJ8Tu5mNRAABBBDINQECfK7tcbYXAQQQQCAnBAjwObGb2UgEEEAAgVwTIMDn2h5nexFAAAEEckKAAJ8Tu5mNRAABBBDINQECfK7tcbYXAQQQQCAnBAjwObGb2UgEEEAAgVwTIMDn2h5nexFAAAEEckKAAJ8Tu5mNRAABBBDINQECfK7tcbYXAQQQQCAnBAjwObGb2UgEEEAAgVwTIMDn2h5nexFAAAEEckKgbE5sJRuJAAIIIJATAm2ObmufzxzrtlXDuZwI8Lm899l2BBBAIMsE9tp9L6tetYbbKg3ncsqIKvpJkybZrFmzcnk/se0IIIAAAgjskECkS/ATJkywjh07Ws2aNW39+vXmeZ69+uqr1qBBgx3aSGZGAAEEEEAg1wQiXYK/++67rVu3bjZmzBibOHGitWjRwh599NFc20dsLwIIIIAAAjssEOkS/FVXXWXHH398bKPq169vw4YNi71nAAEEEEAAAQQSC0Q6wLdp0yaW63Xr1tmAAQNciT420h/o2rWrbdiwIX5UnuHSpUu7av48I3mDAAIIIIBAlgtEOsAH9gru7du3t3r16lnPnj2D0e5VQT8s9e3bN2wy0xBAAAEEEMhKgchcg+/Ro4eptK2/6tWrx7B//vlna9mypVWoUMHeeustK1s2I85JYvlnAAEEEEAAgZIQiEy0vO2226x79+7OIAjiP/74o7Vq1cqaNWtmAwcOJLiXxDeEz0QAAQQQyEiByAT4WrVqmf7iU5cuXVy1/AMPPGDLly93k8qUKZOnhB8/P8MIIIAAAggg8LtAZAJ8/h0ydepUGzlypBtdu3bt2OS6deva/PnzY+8ZQAABBBBAAIHtBSIb4Js2beo6ttk+y4xBAAEEEEAgucAxjY5LPjGHpkQ2wOfQPmBTEUAAAQTSKND6qHPSuLbMXVVkWtFnLiE5RwABBBBAIHoCBPjo7RNyhAACCCCAQJEFCPBFJmQFCCCAAAIIRE+AAB+9fUKOEEAAAQQQKLIAAb7IhKwAAQQQQACB6AkQ4KO3T8gRAggggAACRRYgwBeZkBUggAACCCAQPQECfPT2CTlCAAEEEECgyAIE+CITsgIEEEAAAQSiJ0CAj94+IUcIIIAAAggUWYAAX2RCVoAAAggggED0BAjw0dsn5AgBBBBAAIEiCxDgi0zIChBAAAEEEIieAAE+evuEHCGAAAIIIFBkAQJ8kQlZAQIIIIAAAtETIMBHb5+QIwQQQAABBIosQIAvMiErQAABBBBAIHoCBPjo7RNyhAACCCCAQJEFCPBFJmQFCCCAAAIIRE+AAB+9fUKOEEAAAQQQKLIAAb7IhKwAAQQQQACB6AkQ4KO3T8gRAggggAACRRYgwBeZkBUggAACCCAQPQECfPT2CTlCAAEEEECgyAIE+CITsgIEEEAAAQSiJ0CAj94+IUcIIIAAAggUWYAAX2RCVoAAAggggED0BAjw0dsn5AgBBBBAAIEiCxDgi0zIChBAAAEEEIieAAE+evuEHCGAAAIIIFBkAQJ8kQlZAQIIIIAAAtETIMBHb5+QIwQQQAABBIosQIAvMiErQAABBBBAIHoCBPjo7RNyhAACCCCAQJEFCPBFJmQFCCCAAAIIRE+AAB+9fUKOEEAAAQQQKLIAAb7IhKwAAQQQQACB6AkQ4KO3T8gRAggggAACRRYgwBeZkBUggAACCCAQPQECfPT2CTlCAAEEEECgyAIE+CITsgIEEEAAAQSiJ0CAj94+IUcIIIAAAggUWYAAX2RCVoAAAggggED0BAjw0dsn5AgBBBBAAIEiCxDgi0zIChBAAAEEEIieAAE+evuEHCGAAAIIIFBkgcgHeM/z7LPPPrNp06bZtm3birzBrAABBBBAAIFcECgb5Y2cOnWqnX322XbQQQfZggULrF69evb6669bpUqVopxt8oYAAggggECJC0S6BN+zZ0/r0aOHvffeezZz5kybN2+eDR06tMTRyAACCCCAAAJRF4h0CV6l9QoVKjjDTZs22dq1a6106Uifk0R9f5M/BBBAAIEcEYh0gK9cubLbDf369bMBAwbYsccea+eff36eXdO1a1fbsGFDnnHxb3RC0LFjx/hRDCOAAAIIIJD1ApEO8IG+SvFNmza1sWPH2rhx46xly5bBJBf4Y28SDPTt2zfBWEYhgAACCCCQ3QKRqe/WtXaVtvVXvXr1POqXX365DRo0yDp06GB33XVXnmm8QQABBBBAAIHtBSIT4G+77TabMmWK+xs9erRt2bLFbrjhBluyZEks102aNLHZs2fH3jOAAAIIIIAAAokFIlNFX6tWLdNffFq6dKnde++99s9//tNWrlxpuhZ/5plnxs/CMAIIIIAAAggkEIhMCT5B3uyee+6xZcuWWYMGDaxOnTqu6v6xxx5LNCvjEEAAAQQQQCBOIDIl+Lg8xQb3339/17HNqlWrrFSpUha0qo/NwAACCCCAAAIIJBSIdIAPclylSpVgkFcEEEAAAQQQKIBApKvoC5B/ZkEAAQQQQACBBAIE+AQojEIAAQQQQCDTBQjwmb4HyT8CCCCAAAIJBAjwCVAYhQACCCCAQKYLEOAzfQ+SfwQQQAABBBIIEOAToDAKAQQQQACBTBcgwGf6HiT/CCCAAAIIJBAgwCdAYRQCCCCAAAKZLkCAz/Q9SP4RQAABBBBIIECAT4DCKAQQQAABBDJdgACf6XuQ/COAAAIIIJBAgACfAIVRCCCAAAIIZLpARjxspijINWvWtNtvv93Kls36TbVNmzbZ1q1brVKlSkUhY9k0CHieZ2vWrMmZJyDqaY+J0m677WZ//vOfE03KunFr1651x16ZMmWybtsybYN+++03l+UKFSpkWtYLld/DDjss4XKl/B8iL+EURmacwKeffmqzZ8+2rl27Zlzesy3D69evt169eln//v2zbdPYniQCt912m1111VW23377JZmD0TtL4PXXX3cf1a5du531kZH8HKroI7lbyBQCCCCAAAJFEyDAF82PpRFAAAEEEIikAAE+kruFTCGAAAIIIFA0AQJ80fxYGgEEEEAAgUgKEOAjuVvIFAIIIIAAAkUTIMAXzY+lEUAAAQQQiKQAAT6Su4VMIYAAAgggUDQB7oMvml+kll65cqWtW7fO9t1330jlKxczs23bNps5c6Y1adIkFzc/J7d5zpw57h74ihUr5uT2R2mjf/zxR5edGjVqRClbOz0vBPidTs4HIoAAAgggUPwCZe7yU/F/DJ8QJrB06VL7+OOPXS906onuhx9+MHUwuMcee4Qttt00rePtt9+2vffee4eXjV+ZPjtZ16Px8+XisHoL3LJli+25556xzZ88ebJ9/fXX1rBhw9g41aSMHDnS6tevb5999pmVK1fOdt9999h0BqIjMHHiRJswYYI7/r799ltbsWKF21c7UhJXN7XDhg2zMWPG2BFHHGGlSxf+6ifHX+LvhnqHHDFihKsl0fEUpHfffdfVXO6zzz7BKNN+1G/pXnvt5ZZp1KhRbFpODfhfJlIJCwwdOtTzv7DeUUcd5R155JFegwYNvPLly3vnnHOO5/dnXqDc+V0zervuuqt36aWXelOnTi3QMolmeuGFF7y777470STG+QKdO3f2rrjiijwWzZo18/wfdO+7776LjR8+fLjnPwfBvfd/8L1XXnklNo2BaAlccMEFnl+V644/7Us/UHj+yZj30EMPFTijF110keef4Hl+v/sFXibRjBx/iVR+H7d582avcuXK3nvvvRebyQ/k7tjTMRafLr74Yu+6667z5s6dq67YPf+SWfzknBku/GlmTp0GFf/GVqtWzb766isbP368+V9K97d48WLr3r17gT5cy5177rk2ePBgO/TQQwu0TKKZRo8e7c6GE01jnFmrVq1ciTywWLZsmS1YsMDZq3QRJDmefvrp7q1KiH4QCSbxGkGBCy+80B1/qolRjdqLL75o99xzj73xxhsFyq1qAB5++GH717/+VaD5k83E8ZdMxtwDw04++WQbO3ZsbCbVkmnfzZo1y3QsBik4/vzCkqsNzdUaSQJ88I2I2Ksayj344IPmn9Hb8uXLXe6mTZtmJ510klWpUsUF8bfeesuN90sa7qEm77zzjh1//PGh82riwoULTQ9hUJWWqprbtGnjftSee+4580ua9vTTT9s111zj1sO/vAKnnnqqq/pTNa6SX5qwU045xc466yxTVWGQgh8YvT/vvPNcNaHcW7ZsaU888YTVq1fP+fu1JcEivEZIQMeEXyq3f/zjH7FcJTv+tP/nz59v1157rTsp0AIDBw40VQtXrVrVWrdu7U4CgxXpe+LX1LknDerBNL1793aTOP4CoeSvOv7iA7xOqtu2bWsnnHCCuySmJVVA0m+mTgYWLVpkBx10kAvy//73v+2OO+4wv3TvLmEecsghpuM0q1PO1FVEeENVRR9U58Zn89dff/X8M0/P/xJ6q1evdtWId955p+cHF8//MXDVVd988423YcMG7/rrr/e6dOnirVq1KnRerd//QfL8p155fqt7zz/z9fxHDXo33XST5z9i0a1D6/KvIcdnheE4Ab9lvKcqeCW/9OA99dRTnl+K9/zH9Lp9of3mP57Y81vyunmCKvqgOrFTp07evHnzXFWj5psyZYqbj38lI6AqelXn5k/+ia5XvXp1Nzrs+NO0unXruu+EjkX/RNvzA7vnBx/Prw3wrr76au/www/3/Ec5u/e6/Kbvj39N2fODvatC9mvgOP7y74AE76dPn+7tsssunqrrN27c6PmPI/Z+/vln75FHHvE6dOjglvBPrjw/uLvh+Cr6+++/3x2XAwYMcMdmz549vQMPPDDBp2TPKErwET59U0ndv65u/hfY3nzzTdfwrUePHu5VpQKdoeqsX42B9Nxj/4fDlQrC5tXmPvbYY646Uev3A4z5X3LXsE/L60/r8g+iCMuUbNZUTa9ShP+DbR988IGrildJTH8arz9dJvGDw3YZ1e1zqplR4ztV4etVJQ5S9ARq1aplv/zyiyv9hR1TajypRnU6VnUsqqR4/vnn29FHH+2OJz1GVlXIqv7XvF9++aW7pKNl1CBWt3KpYS3HX+rvQOPGjV2tyKRJk1zpW+9VC6lj6cMPP3T7Kr72LP8ajznmGLvyyivdsem3pzH/RDv/LFn1vmxWbU2WbYyqgdU61y8duOpfv3RuzZs3z7OVtWvXzvNeb77//nsLm1fTVK2lg0TrVtDhfu3tGJOOUDXh3/72N3fNVj/QMlTSj8y4cePcPguuvydaSXxrX78E4lrlJ5qPcSUroGp3nbTp+m2qYyo+p1pOd07o8k2Q9D356aefXNX8qFGjzK/FsSVLlrjj2a85c8dgMC+v4QJBNb2q34PjTCfUOrlSXwQK8H6NTMKV5NqxR4BP+DWIxki/6t6d4essVY14dKavM86gwYgalajUkD+p5JhsXt2+pevF9913n/kt712JX7UCKqmQCiagmhNdx1MDn+AHRktquH///uZX0VufPn2SrizYf0lnYEIkBIYMGWLa10phx5SbIe6f5tV3QY3ugqSgr3Y1r776qqnNjI69Fi1aWJkyZdy6dZJNKpiAatB07X3GjBn2z3/+M7bQaaed5m5V1G+cfzdEbHz8QPyx51fEx0/KyuHSWblVGbhRqu5V6139+deZzL9O5Brf3H777e7M9IwzznCtRAcNGuSqodRTk39dz90/n39zw+b1rxGaSgz64fJvOXGNf/zrgbZp0ya3GlUhBg3I8q+X978LyKhp06b2n//8J0+Al6mqYVUdq0Y/pMwRUFDQsac7V3Q3ixqZ6u6Hm2++2W1E2DGVfytVO6bGsboPW0kn6qoa9m95dSd/qlI+9thjXXB/6aWXXIMw/5qym5fjzzGE/lMJXqV0NVqVY5B0UqW7GHQCEB/Ig+m5+EoJPiJ7XdV3uuanpGpbdZqiEoCuFympGlg/Gn6DHVPQ17Vz/35s8++Vd9Pj/6WaV6129YOl6iqtR9eiFOSV1Mpbrb5V1aVOO0iJBfQj8sADDzivYA7tN7VnUK2KrqeSMkdAJ2v6U2DQLau6fq6Oo7Q/lVIdU/FbqltbdfzoJFDHtNq66MRcHVepZb5uwVPVv74vak1/5plnutKo1sHxFy+ZeFiXJXV3gvaRfr+CpBK8Ln3E16oF03L1la5qM3DPq2peVfAFOUtNNq9KDLoWrx+z/EnTVKOwIz155V8H7xHIVoFkx1T+7VWtmI4xXX/Pn3QZR41Z/Tsv8k8yjr/tSBhRSAECfCHhWAwBBBBAAIEoC3ANPsp7h7whgAACCCBQSAECfCHhWAwBBBBAAIEoCxDgo7x3yBsCCCCAAAKFFCDAFxKOxRBAAAEEEIiyAAE+ynuHvCGAAAIIIFBIAQJ8IeFYDAEEEEAAgSgL/D/IoFNcQBSnYAAAAABJRU5ErkJggg==" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_a12.pdf&quot;, 
       width = 9, height = 3.5)


## Table A4 ----
models_strongholds &lt;- list()
models_strongholds[[&#39;M1: General elections (incumbency, strongholds)&#39;]] &lt;- lm_cand_share_general_strongholds_base
models_strongholds[[&#39;M2: General elections (incumbency, strongholds)&#39;]] &lt;- lm_cand_share_general_strongholds
models_strongholds[[&#39;M3: Local elections (incumbency, strongholds)&#39;]] &lt;- lm_cand_share_local_strongholds_base
models_strongholds[[&#39;M4: Local elections (incumbency, strongholds)&#39;]] &lt;- lm_cand_share_local_strongholds
models_strongholds[[&#39;M5: General elections (government status, strongholds)&#39;]] &lt;- lm_govstatus_share_general_strongholds_base
models_strongholds[[&#39;M6: General elections (government status, strongholds)&#39;]] &lt;- lm_govstatus_share_general_strongholds


rename_coefs_strongholds &lt;- c(&quot;Intercept&quot; = &quot;(Intercept)&quot;,
                              &quot;winner_loser_beforeWin&quot; = &quot;Win (ref. = Defeat)&quot;,
                              &quot;incumbentIncumbent (Candidate elected in t-1)&quot; = &quot;Candidate elected in t-1&quot;,
                              &quot;winner_loser_beforeWin:incumbentIncumbent (Candidate elected in t-1)&quot; = &quot;Win : Candidate elected in t-1&quot;,
                              &quot;incumbency_status_partyParty: Government&quot; = &quot;Candidate&#39;s party in government&quot;,
                              &quot;winner_loser_beforeWin:incumbency_status_partyParty: Government&quot; = &quot;Win x Candidate&#39;s party in government&quot;)

rows_add_strongholds &lt;- tribble(~term, ~m1, ~m2, ~m3, ~m4, ~m5, ~m6,
                                &#39;Election FE&#39;, &#39;&#39;, &#39;✓&#39;, &#39;&#39;, &#39;✓&#39;, &#39;&#39;, &#39;✓&#39;,
                                &#39;Party FE&#39;, &#39;&#39;, &#39;✓&#39;, &#39;&#39;, &#39;✓&#39;, &#39;&#39;, &#39;✓&#39;,
                                &#39;County FE&#39;, &#39;&#39;, &#39;✓&#39;, &#39;&#39;, &#39;✓&#39;, &#39;&#39;, &#39;✓&#39;)

attr(rows_add_strongholds, &#39;position&#39;) &lt;- c(11, 12, 13)


modelsummary(models_strongholds,
             add_rows = rows_add_strongholds,
             statistic = &quot;conf.int&quot;,
             fmt = &quot;%.2f&quot;,
             stars = c(&#39;*&#39; = .05, &#39;**&#39; = .01, &#39;***&#39; = .001),
             coef_omit = &quot;(party_recoded*)|(election_*)|(county_gaa*)|(Intercept*)&quot;,
             coef_rename = rename_coefs_strongholds,
             gof_omit = &quot;se_type&quot;,
             align = &quot;lll&quot;)
</code></pre>

<pre><code>## 1 coefficient  not defined because the design matrix is rank deficient
</code></pre>

<pre><code>## 1 coefficient  not defined because the design matrix is rank deficient
</code></pre>

<table class="table" style="width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:left;"> M1: General elections (incumbency, strongholds) </th>
   <th style="text-align:left;"> M2: General elections (incumbency, strongholds) </th>
   <th style="text-align:left;"> M3: Local elections (incumbency, strongholds) </th>
   <th style="text-align:left;"> M4: Local elections (incumbency, strongholds) </th>
   <th style="text-align:left;"> M5: General elections (government status, strongholds) </th>
   <th style="text-align:left;"> M6: General elections (government status, strongholds) </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Win (ref. = Defeat) </td>
   <td style="text-align:left;"> -0.28 </td>
   <td style="text-align:left;"> 1.57 </td>
   <td style="text-align:left;"> -0.06 </td>
   <td style="text-align:left;"> -0.47 </td>
   <td style="text-align:left;"> -1.99* </td>
   <td style="text-align:left;"> -1.17 </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> [-1.80, 1.24] </td>
   <td style="text-align:left;"> [-1.17, 4.31] </td>
   <td style="text-align:left;"> [-0.93, 0.81] </td>
   <td style="text-align:left;"> [-1.59, 0.65] </td>
   <td style="text-align:left;"> [-3.65, -0.32] </td>
   <td style="text-align:left;"> [-3.86, 1.51] </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Candidate elected in t-1 </td>
   <td style="text-align:left;"> -0.32 </td>
   <td style="text-align:left;"> -0.37 </td>
   <td style="text-align:left;"> -1.06** </td>
   <td style="text-align:left;"> -1.60*** </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;">  </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> [-1.71, 1.07] </td>
   <td style="text-align:left;"> [-1.97, 1.23] </td>
   <td style="text-align:left;"> [-1.80, -0.32] </td>
   <td style="text-align:left;"> [-2.39, -0.81] </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;">  </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Win  ×  Candidate elected in t-1 </td>
   <td style="text-align:left;"> -1.94* </td>
   <td style="text-align:left;"> -2.64** </td>
   <td style="text-align:left;"> -0.10 </td>
   <td style="text-align:left;"> 0.28 </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;">  </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> [-3.54, -0.34] </td>
   <td style="text-align:left;"> [-4.23, -1.05] </td>
   <td style="text-align:left;"> [-1.20, 1.00] </td>
   <td style="text-align:left;"> [-0.77, 1.34] </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;">  </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Candidate&rsquo;s party in government </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> 0.16 </td>
   <td style="text-align:left;"> -0.54 </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> [-1.57, 1.90] </td>
   <td style="text-align:left;"> [-2.59, 1.51] </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Win x Candidate&rsquo;s party in government </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> 0.98 </td>
   <td style="text-align:left;"> 1.56 </td>
  </tr>
  <tr>
   <td style="text-align:left;box-shadow: 0px 1px">  </td>
   <td style="text-align:left;box-shadow: 0px 1px">  </td>
   <td style="text-align:left;box-shadow: 0px 1px">  </td>
   <td style="text-align:left;box-shadow: 0px 1px">  </td>
   <td style="text-align:left;box-shadow: 0px 1px">  </td>
   <td style="text-align:left;box-shadow: 0px 1px"> [-1.30, 3.26] </td>
   <td style="text-align:left;box-shadow: 0px 1px"> [-1.43, 4.55] </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Election FE </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Party FE </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> County FE </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Num.Obs. </td>
   <td style="text-align:left;"> 437 </td>
   <td style="text-align:left;"> 437 </td>
   <td style="text-align:left;"> 1838 </td>
   <td style="text-align:left;"> 1838 </td>
   <td style="text-align:left;"> 437 </td>
   <td style="text-align:left;"> 437 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> R2 </td>
   <td style="text-align:left;"> 0.039 </td>
   <td style="text-align:left;"> 0.228 </td>
   <td style="text-align:left;"> 0.011 </td>
   <td style="text-align:left;"> 0.110 </td>
   <td style="text-align:left;"> 0.023 </td>
   <td style="text-align:left;"> 0.201 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> R2 Adj. </td>
   <td style="text-align:left;"> 0.032 </td>
   <td style="text-align:left;"> 0.141 </td>
   <td style="text-align:left;"> 0.010 </td>
   <td style="text-align:left;"> 0.086 </td>
   <td style="text-align:left;"> 0.017 </td>
   <td style="text-align:left;"> 0.111 </td>
  </tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; border:0;" colspan="100%">
<sup></sup> * p &lt; 0.05, ** p &lt; 0.01, *** p &lt; 0.001</td>
</tr>
</tfoot>
</table>

<pre><code class="r">modelsummary(models_strongholds,
             add_rows = rows_add_strongholds,
             statistic = &quot;conf.int&quot;,
             fmt = &quot;%.2f&quot;,
             stars = c(&#39;*&#39; = .05, &#39;**&#39; = .01, &#39;***&#39; = .001),
             coef_omit = &quot;(party_recoded*)|(election_*)|(county_gaa*)|(Intercept*)&quot;,
             coef_rename = rename_coefs_strongholds,
             gof_omit = &quot;se_type&quot;,
             align = &quot;llllll&quot;,
             output = &quot;tab_a04.docx&quot;)
</code></pre>

<pre><code>## 1 coefficient  not defined because the design matrix is rank deficient
## 
## 1 coefficient  not defined because the design matrix is rank deficient
</code></pre>

<pre><code>## [1] &quot;tab_a04.docx&quot;
</code></pre>

<pre><code class="r">## Robustness: &quot;Unambiguous&quot; county teams ----

## only use candidates who appear once
## some candidates appear twice in the dataset because their 
## constituency lies in two counties (e.g., candidates in constituency
## Carlow-Kilkenny are matched to the Carlow team and the Kilkenny team)
## In the subsequent analyses, we test whether results change
## when we only consider candidates who are matched with a single team


## note: n_obs == 1 filters only candidates who were matched to a single county team
lm_cand_share_general_unambiguous &lt;- update(lm_cand_share_general_all,
                                            data = filter(dat_general,
                                                          n_obs == 1))

lm_cand_share_local_unambiguous &lt;- update(lm_cand_share_general_all,
                                          data = filter(dat_local,
                                                        n_obs == 1))


lm_cand_share_general_unambiguous_base &lt;- update(lm_cand_share_general_base,
                                                 data = filter(dat_general,
                                                               n_obs == 1))

lm_cand_share_local_unambiguous_base &lt;- update(lm_cand_share_general_base,
                                               data = filter(dat_local,
                                                             n_obs == 1))


## Figure A13 ----
dat_eff_share_general_unambiguous &lt;- ggpredict(lm_cand_share_general_unambiguous_base,
                                               terms = c(&quot;winner_loser_before&quot;,
                                                         &quot;incumbent&quot;))

plot_interaction(dat_eff_share_general_unambiguous)
</code></pre>

<p><img 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w98GX14ulG10DlYsmRJ4EbH/Psky1DQ6bqQdyVfHQ/j1wezTCH1w3tM+0/CaZNNNgncCEXK1ukwBK4n7O/dqKD/AHC94JT7f/7zH59v8ZXRR1BcW5evfNPfv0Ly4KZXgjPPPDOVHqX9//7v/3xnLTs9hYSXr9xSEaVd6L1KF/BxDNMey7isFXPw48ePN9f7Ntdjdsz+Z5zwDS8L+pVGo3uJUn7btWtnThin7jWUKyNlFA1DyThB4H/1n+KWfyewzVVKc0LPVl99da+cE3qSco6GlV0JeSvXM8iIU1MB0tYt1GiKIExL+MyGG25YZtg7dMv+1dC60qvhNSeU/LVrAP1Qc+g3Lo3uxfdD3OmKIpqyEMso4z6SbJtttslwkqa/ht5kNE2hqRQNQ2pYXtMumveUSR/u8xbuv0I4Z6dFaZZxDWXGdILrRZl4Zhv511SBhu1CE6ZF/hW+GKXnS5rWw4cPD71n/OYLb/bs2eVimhH4rzdxZRblP91O0x/pc4MaZnYfJeleMq7dh23GffZNIWWkZ+LSrKkv1SW9K6HR9E6oTV6eODSNkstoOFv1Nr1OuYbbe8+1DDU9LPdx7KcPpNGteq6heffh59/TdH8alg+N6ryMpsXmz59vrgOR8S7pvdx6661D7xm/hdS/Qup4eqDlrR/hs+KaXtc0vRC2ZaonmgoI9YT0jNpOlbnan/R2Ngwv/bfQ8g2fyZcHTZXIjxPw4SM+7bmUd/OFV95yS0WadRHHMMtr6vY3iZeyqnkXErpuuCU2YyrUdKOXJt2osWjSpEnKSkJYc2xxJhT6oR8tUdEzqqyKz32RZby80syVUUMio4+C9I+S9AriPeT5T41r2MiFXjU/d++994a3Gb+ar5MCnjTsd911Vy9MNQffq1cv33i6YSjTPH34AaKH49IohhJ2+lNDJKNrVdwoIzdVhnTjejWpWy3Xkq6D9BA0X6cGXemVfkOUKYRzNh81OkqfyiLdTfduSqZMNPIvnYaw7EIPu7m5OzX+Ej5uCiFnnkP/4W++8KSDUB6mYbjpv3Fllu4v6lrpS9cl0YduZUwhZaTw49Ks90IfWenlJT2SUGAUGofqZvhMVJ70LqreptdDzZNLoS7U98h+TvPzjz32mP8okm6F3tuObo5ZdUrKj/rwcyNDGY+ltythXKpzKnf9ZteR7LYrDKyQ+qcP5nx1PAxPv+WtH+Gz6W2n7MJ86VrlIx2ddKP2QvUmbDfS3bKvCy3f8Ll8eRBn+Ul/n8Jno34LCa885RYVh+ziGOZ6plYIePUgtYQl3bihKa/M4ob/vNCV8E03+lJOV6RJdyv0WiMH6knIqBGSApnWmqtRUM9HhS6Fq9C4IUavfFXoi6XnFEYuI2HuhioznPVlLAVDp0eQsXROnrReXkpVSqMqjTTw5dcNTaXCkECV8lkhRl/eYb4lmGXcUKalC+30cJReKQ2qwQo/AtLLTY2leuzamEfKVDJuGNL/pocZMqkIZ5W54tdv+vI98Qp7Uz7CX//Tu+WGL/2HRtgwS7nR6U349fsaMdGHlhp39dxlpGAnxcdwZCJMr9zyhVdepgqzKkyYRqXP6ZwUHGQu4RMGUJEyCp8Nf6WMJOZ6L9q3b++tJXBDQVgVcShQCX/1rjVylG7cXLFpXwnVDZV3aFTuUo4SO33sa+RDynFOfyQl4FTX0t/d8Nmo31CJUyNYWvooozheeumlyL0k8r0rhdTxdEGs+MpbP/RMPqMw3bRWRr13U3g+b+IVmvAdDO/D3/KWb748qB6rfdE7JMXL0GgppNN58ErDsgvTky+88pZbGF9V/EZ3paoi5BIKQ9q1KqwLL7zQNMTpFMa8MAt7fupJq9JIWEhzVF+1bu600jnQMKwadq1F19pUfZGGQ7kS7Nqkxs01eY1rxe3mSn1DVWjEGpXQ8JaG9qOMBKaGFMMXUX6UZ60OUPxqcMRCcWvNpVN+sWOOOcaPKujLWV/VaszUiOgrVUvr9OEjjdFCjLSKNXw4dOhQP8SvMnBzjjkfVY9G8bg5SL+JiJt/ytgsSD0f5SUcKtdwsVZAyIRpEhPx1seVGrDycpaGsxoVN1dqTonPD5+6OWg/XKcpgmyj1RT6iJHWtdKjXcw07KlREmnfqoemhlarMjQFofdP76GGlNXzy05vvvDKyzQ7vRW5z05jecLQu5PPlLeMssNTr1jvuoSeNnTSR61WzKSbysYRhqV4NF2Vbv7xj3+kPgg1daQPOG1QIuGg+qKhXQlKfRjrHfnpp5/8eyyNeqdQluqZp4eZ61rth1bWKHyNTKquZHdOwmfzvSuF1PGw16j9NpyCmV8BUJ76EaYl7lcjhqqz+thRHG+++abvXIh1+FGcr60rT/kWUsdVD9Ue6iNcIyFOR8Mz16oHmfT0FBJeecotjlW53VyDWSuMlMVcDyxwvePADal6TVhXcD7v0hSWZrOD5zXb3eYUXqvefZF7dyn5uMqQwcn1zFPKZnKQFreel5KOa9T8tWvIA9eQ+zilCCMN/NAobidUvZa4wpbmrBTK9KxMVJxOaGSkQ4oj7mvT/7leQBh06lca+0qT+8hI2enC9aq8oo4UScRDfpROKbC54amUX/dyB66CeTf3QvtVBVJ0cb0U76eQNLoh5cBVAB+P4nIb6vj0RinZKVBpr0oR0A3JeuUjrS4Ilezk7gS6V3iTnTTbXc8pcA1Z4Nbuyzlww/demVF5Ulj5OPuHsv5TGG4I1Ws8q2ykWS2lzNCkK9nJzgmVwA1V+zSLk9zdR0jo3SsFuukEH540td3yx5TyZHZ6CwkvH9NUxL9eZCvZZb/L2e9V9vNRacz2k+v+2GOP9U7pSk7ug8i/c6EWfb4yKuQ9k3az8qn3Qu+nVmzoHVDZyOSLQ0p2Krt8RhrgTsCV8SZlSteR8HErXv2p7rjh+ZRfN08fuJEsX+ddrzOQwqqU4uRX7YbqcHqa9WBop3c5NFL+kia96oiUz6RtHqVFL//53pV8dVxhuNEm/+6GK0Py1Q89k26kZKdVRulGWudheLLXO6h2WXlyIyV+9Y37GE49kq+ty1e+6e+fAs2XB4Xnluql2i21AVL8C012evKFp+fiyi0MN/1X70K6kl0+hunPhtf6kqxVRks9tPwjykjQu+0no5wqbCeBLU1vCdUok8896pl0OwlkLdXJZVQ5tdwsl3HDmIEanqgPhPAZMdMLXxmjyqq4CjVqMHOlSXlWmuKM0yfIYF4RzuIaLk+Kiyt0c70zvwQsvM/+VZr0F2Wy0ys/+cIrL9OoeMtjF5XG8jyfz29FykhhioOWKKbXa7FTA+l6gxnRVjSOMBB9SEg4v/3226FVmV8JVS13zGVcDz7ne5DrmWx71QEt/y3U5HtX8tVx1YX0j3/FW976UUhalQ438hbpNV9bp4fKW7758qB2T+9SlIlKT77wyltuUfGWx66OPLuKgKmhBDREKO1dTRVorgoDgZpGQPPvmjPVVJA2YnIfIl5vRCs+NMVUHp2WQthoykbz6IWcA1FIePiBQLEI1Io5+GLBS0K4mufVnLC2oMVAoCYS0JIrKWdKZ0MKTVq9IKEvpdWqFu7iJ70PzcNnK9vVRLbkKdkE6MEnu/wKSr0U0Nz0Q0rDuKCH8ASBBBJQ710KUHHL3aoiW1IG08cDo2JVQZMwikUAAV8ssoQLAQhAAAIQqEYCDNFXI3yihgAEIAABCBSLQI3f6Mbt4ezXxoYbpxQLJOFCoDYT0Ny3djnMNloj7jSjs625hwAEqpCA1uJn7/2g4Gu8gJcyzCB3HrO2E8VAAALFIRBuOJQduo771WYuGAhAoDgE3HJiL+OiBDxD9MVhTqgQgAAEIACBaiWAgK9W/EQOAQhAAAIQKA4BBHxxuBIqBCAAAQhAoFoJIOCrFT+RQwACEIAABIpDAAFfHK6ECgEIQAACEKhWAgj4asVP5BCAAAQgAIHiEEDAF4croUIAAhCAAASqlQACvlrxEzkEIAABCECgOAQQ8MXhSqgQgAAEIACBaiWAgK9W/EQOAQhAAAIQKA4BBHxxuBIqBCAAAQhAoFoJIOCrFT+RQwACEIAABIpDAAFfHK6ECgEIQAACEKhWAgj4asVP5BCAAAQgAIHiECh5AR8Egb3++uv24Ycf2sqVK4tDgVAhAAEIQAACNYxASZ8HP3HiRDvggAOsa9eupnOlO3bsaA8//LA1adKkhhUD2YEABCAAAQhULYGS7sEPHDjQBgwYYE8//bR9+umn9tVXX9kDDzxQtQQIDQIQgAAEIFADCZR0D1699UaNGnnsS5cutfnz51vduiX9TVIDXxGyBAEIQAACSSRQ0gK+efPmnunw4cNt5MiRtv3229thhx2WwfnEE0+0RYsWZdil3+iDoH///ulWXEMAAhCAAARqPIGSFvAhffXie/ToYa+99pq9+eab1qtXr9DJC/7UTcTFsGHDImyxggAEIAABCNRsAokY7z7uuOPs9ttvt379+tngwYNrdomQOwhAAAIQgEAVEChZAb98+XI7++yz7fvvv09ls3v37jZp0qTUPRcQgAAEIAABCEQTKFkBX79+ffvxxx9t6NChtmLFCps1a5ZpLn7fffeNzgm2EIAABCAAAQikCJSsgFcKL730Ups2bZp17tzZ1l9/fWvVqpVde+21qcRzAQEIQAACEIBANIGSVrLbYIMN/MY2c+fOtTp16lioVR+dFWwhAAEIQAACEAgJlLSADxPZokWL8JJfCEAAAhCAAAQKIFDSQ/QFpB8vEIAABCAAAQhEEEDAR0DBCgIQgAAEIJB0Agj4pJcg6YcABCAAAQhEEEDAR0DBCgIQgAAEIJB0Agj4pJcg6YcABCAAAQhEEEDAR0DBCgIQgAAEIJB0Agj4pJcg6YcABCAAAQhEEEDAR0DBCgIQgAAEIJB0Agj4pJcg6YcABCAAAQhEEEDAR0DBCgIQgAAEIJB0Agj4pJcg6YcABCAAAQhEEEDAR0DBCgIQgAAEIJB0Agj4pJcg6YcABCAAAQhEEEDAR0DBCgIQgAAEIJB0Agj4pJcg6YcABCAAAQhEEEDAR0DBCgIQgAAEIJB0Agj4pJcg6YcABCAAAQhEEEDAR0DBCgIQgAAEIJB0Agj4pJcg6YcABCAAAQhEEEDAR0DBCgIQgAAEIJB0Agj4pJcg6YcABCAAAQhEEEDAR0DBCgIQgAAEIJB0Agj4pJcg6YcABCAAAQhEEEDAR0DBCgIQgAAEIJB0Agj4pJcg6YcABCAAAQhEEEDAR0DBCgIQgAAEIJB0Agj4pJcg6YcABCAAAQhEEEDAR0DBCgIQgAAEIJB0Agj4pJcg6YcABCAAgaIROPWm423G3BlFC7+YASPgi0mXsCEAAQhAINEEHn/nYVuweH4i84CAT2SxkWgIQAACEIBAPAEEfDwfXCEAAQhAAAKJJICAT2SxkWgIQAACEIBAPAEEfDwfXCEAAQhAAAKJJICAT2SxkWgIQAACEIBAPAEEfDwfXCEAAQhAAAKJJICAT2SxkWgIQAACEIBAPAEEfDwfXCEAAQhAoJYSeP/Ld23ZimX21qQ3EkkAAZ/IYiPREIAABCBQbAJn3Xqa2+RmgZ38r2OKHVVRwkfAFwUrgUIAAhCAAASqlwACvnr5EzsEIAABCECgKAQQ8EXBSqAQgAAEIACB6iWAgK9e/sQOAQhAAAIQKAoBBHxRsBIoBCAAAQhAoHoJIOCrlz+xQwACEIAABIpCAAFfFKwECgEIQAACEKheAgj46uVP7BCAAAQgAIGiEEDAFwUrgUIAAhCAAASqlwACvnr5EzsEIAABCECgKAQQ8EXBSqAQgAAEIACB6iWAgK9e/sQOAQhAAAIQKAoBBHxRsBIoBCAAAQhAoHoJIOCrlz+xQwACEIAABIpCAAFfFKwECgEIQAACEKheAgj46uVP7BCAAAQgAIGiEEiEgB83bpx99tlnRQFAoBCAAAQgAIGaSKB+KWfqvffes/79+1vbtm1t4cKFFgSBPfjgg9a5c+dSTjZpgwAEIAABCFQ7gZLuwV9yySV20kkn2auvvmrvv/++bbvttnbNNddUOzQSAAEIQAACECh1AiXdgz/55JNtxx13TDHs1KmTjRkzJnXPBQQgAAEIQAAC0QRKWsDvv//+qVQvWLDARo4c6Xv0KUt3IXsN3ecyy5cvz+WEPQQgAAEIQKDGEihpAR9SlxA/5JBDrGPHjjZw4MDQ2v9edNFFtnjx4gy79JslS5ak33INAQhAAAIQqBUESl7Az5492/bdd19r06aN3X///Va/fmaSr7322tiCGjZsWKw7jhCAAAQgAIGaSKCkleymT59uu+22m3Xt2tXPvTdu3LgmlgF5ggAEIAABCFQ5gczucJUHX7kAjznmGD8sf8UVV9jMmTN9YPXq1bNWrVpVLmCehgAEIAABCNRwAiUr4CdOnGhjx471+Nu1a5cqhg4dOtiUKVNS91xAAAIQgAAEIFCWQMkK+B49esRqx5fNCjYQgAAEIAABCIQESnoOPkwkvxCAAAQgAAEIlI8AAr58vPANAQhAAAIQSAQBBHwiiolEQgACEIAABMpHAAFfPl74hgAEahCBGe6UyvH33luDckRWIPAbAQT8byy4ggAEahmBaR9+aO/eOqqW5Zrs1hYCCPjaUtLkEwIQgAAEahUBBHytKm4yCwEIQAACtYUAAr62lDT5hAAEIACBWkUAAV+ripvMQgACEIBAbSGAgK8tJU0+IQABCECgVhFAwFdzca9Ytsw+GD26mlNB9BCAAAQgUNMIIOCruUSXLVpk9/3xGFu5cmU1p4ToIQCB2k7gp59+siFDhtR2DDUm/wj4GlOUZAQCEIBA5Qj8/PPPdt1111UuEJ4uGQII+JIpChICAQisSgI/T51qb434t03/5FMbd9ddqzJq4oLAKiGAgF8lmIkEAhAoNQILZ8+2yS+8aPN++MGmvPFmqSWvWtLz4IMP2pIlS+zxxx+vlviJtGoJIOCrliehQQACEEgsgfPPP98WLlxoQ4cOTWweqjLhS5Yt8cEFQWDLVyyvyqBXSVgI+FWCmUggAAEIQCApBD78ZqIdenkf+/z7ST7JgQW218W72MsfvpCULPh0IuATVVwkFgIQgAAEikngq2mT7fArDrSnxz1lS5f/rwev+N75/C3rM6S3PfBack4fRMAX800hbAhAAAIQSAyBBYsX2BFX9rWps77NmebLHxhsU6Z/ndO9lBwQ8KVUGqQFAhCAAASqjcCH30ywn+bPjo3/6+lf2fMTn431UyqOCPhSKQnSAQEIQAAC1Urgmxlf24y502PTIGW7D6eMj/VTKo4I+FIpCdIBAQhAAALVSqBh/UbWsF7DvGlo2qhpXj+l4AEBXwqlQBogAAEIQKDaCXRdr5u1XXPd2HS0aNrStu+6U6yfUnFEwJdKSZAOCEAAAhCoVgJd2nW1nbvvZvXr1s+ZjoYNGlqvzXrndC8lBwR8NZfGVy+97FPw1cv/+63m5BA9BCAAgVpLoG7dunbtCTdat/ab2mqNm2VwaNywsfXstIWNu/4za9Yk0y3DYwndIOCruTAePP4En4KHTjipmlNC9BCAAAQgIEH+yhXv2LCTRpiG40Nzye//bo/97RlrudpvdqFbqf4i4Eu1ZEgXBCAAgVVE4Msvv7QBAwakjq3+6KOP/Ha18+fPX0UpKK1o6terb0fsfKRtuO5GPmF169S1AfufaWutvlZpJTRPahDweQDhDAEIQKAmE5g8ebLtuuuuNnz48FQ2tR/9pZdeah06dLDZ7lAeTDIJIOCTWW6kGgIQgEClCcyaNcu22WYb+8GdqKcDVdLN0qVLbc6cOXb88cfb8uXJO2glPS+19RoBX1tLnnxDAAK1nsCzzz7rj4fNBUJCf9y4cTZx4sRcXrAvYQII+BIuHJIGAQhAoJgE3n77bVuwYEFsFD/++KNpjh6TPAII+OSVGSmGAAQgsMoIqBdfp06dVRYfEVUdAQR81bEkJAhAAAKJIrDllltakyZNYtPcpk0br2wX6wnHkiSAgC/JYiFREIAABIpPYM8997SNN944NqK2bdva5ptvHusHx9IkgIAvzXIhVRCAAASKTkDCe+TIkZHxaFe37t2729NPP20NG+Y/gCUyECyrlQACvlrxEzkEIACB6iWw9dZb28cff2yHH354KiGNGzf2G9+MHTvW1lhjjZQ9F8kigIBPVnmRWghAAAJVTqBbt2523333pZTpNt10Uxs2bJitt956VR4XAa46Agj4VceamCAAAQiUNIFQW17D85jkE6AUk1+G5AACEIAABCBQhgACvgwSLCAAAQhAAALJJ4CAT34ZkgMIQAACEIBAGQL1y9hEWDz55JP21FNP2WabbWY9e/a0tdde2zbYYIMIn1hBAAIQgAAEIFAKBPL24C+44AI74YQT/GED48ePt2+//da22GILe+ONN0oh/aQBAhCAAAQgAIEIArE9+J9//tmuvvpqmzRpkr311lv26quvWr9+/fwRgnfffbftsMMOEUFiBQEIQAACEIBAdROI7cFPnTrVunTpYp06dcpIp9ZMTpgwIcOOGwhAAAIQgAAESodArIDfaKONbMqUKfb888+nUrxw4UK7/vrrrUePHik7LiAAAQhAAAIQKC0CsUP02q5wxIgR1qdPH2vZsqU1aNDARo8eba1atWIOvrTKkdRAAAIQgAAEMgjECnj5lFCXct0rr7xiM2fO9Nrzffv2NQl/DAQgAAEI1BwC5557rt+i9rTTTqs5marFOYkV8JMnT7b+/fvb3Llz7aSTTqrFmMg6BCAAgZpPQO38zTffbEcffXTNz2wtyGHsHPz6669vO+64o91+++02e/bsWoCDLEIAAhCAAARqBoFYAS+FOinZnXHGGX5zm/r161v4p549BgIQgAAEag6B5s2b03uvOcVpsUP0KuznnnsuMrurr756pD2WEIAABCCQTALapVSrpDA1g0CsgNeRgVoHj4EABCAAAQhAIFkEYgW8srJs2TJ77bXXvKKd7lesWGEaul+5cqX96U9/khUGAhCAAAQgAIESI5BXwO+xxx72zjvvWJMmTaxp06am7WuXL1/ul1KUWF5IDgQgAAEIQAACvxKIFfDfffedvffee/bDDz/Y8OHDrVmzZjZgwAA76qijrG3btkCEAAQgAAEIQKBECcRq0f/000/+iNg111zTnyD3+uuvey36888/30aNGlWiWSJZEIAABCAAAQjECnid+a6T5NST197zmovXnPy8efNs8eLFq5SeTrLThjsYCEAAAhCAAATyE4gV8KuttppfA7/lllvaeuutZ127djVdH3TQQabtaleV0VK9Xr16+bPoV1WcxAMBCEAAAhBIMoHYOXhlbMiQIXbMMcf4PD7yyCN2xx132CabbGK9e/f2dsX8b8GCBTZo0CC76667LAiCYkZF2BCAQC0jsNKtCArNCjcyiYFATSMQ24NXZjUkr7n4Bx54wJ544gm/o50OndF8fLHNuHHj/AE3H3zwgTVs2LDY0RE+BCBQSwhMcO3ZA8edkMrtRw8+ZE/85a+2fMmSlB0XEEg6gbw9+IMPPtjGjh1r3bt3zxCy6sFrn/pimp122sn0F2duu+02W7p0aU4vX3/9dU43HCAAgdpH4AM3IvjogNNtcZpOz6I5c+zVa6+18XffY2d9NNGauOOxMRBIOoFYAT916lR74YUXTL+luixO0wVal5/LaIkfBgIQgIAIfO2UdR8++VRb6qb/sk2wfIXNnz7dxl54kfX91w1Wp06dbC/cQyBRBGIFvOa9daJcqQp3kd5uu+1igWt4HwMBCEBABMa5HnqUcA/prHSdhS+ff94WumnJ1dZaK7TmFwKJJBA7B9++fXs/NH/DDTf4efhE5pBEQwACEPiVwLSJE/OyWOZ693OY2svLCQ+lTyBSwB999NF+eEpDVA8//LBfKreW+5rVffh3+OGHl37uSCEEIACBNAJ13ZHX+Uwdd8hWIf7yhYM7BKqbQOTbfvXVV/vlaXGJ07a1GAhAAAJJItDeTelpHt5iVt02bLa6rdm5c5KyRVohEEkgsgffunVr23DDDf2fdo979NFH/fWTTz5pO+ywg5122mn+4JnIEItkuWjRIr9tbpGCJ1gIQKAWENjqT3+MFd51GzSwLY78vTVu3rwW0CCLNZ1ApIAPMy3t9AMPPNAvQ5OgP+ecc+ySSy4xDdcPHjw49MYvBCAAgUQQaN2tm/UbdWtkWpuuvZZt0mc/2/3CCyLdsYRA0gjECvjJkyf7jW10uIx679qu9tRTT7WBAwf6U+aSllnSCwEIQKDzLrvY/00YZ90P/m277Rbt17cDr7/e/vDgAwCCQIrAOs1beb2zVi1bp+ySdBEr4Os6ZZP58+f7/GibWm16I/P5559bly5d/DX/QQACEEgagbbu8KzeF/8tlexN9t/fD83XrVcvZccFBB48/3Fr0bSFPXup09tIoIlUsgvzsdFGG5nm43v27GlffvmlvfXWW/bQQw/5Xvy9994beuMXAhCAAAQgAIESIxAr4JXWZ555xp566inbdNNN/SEz2hb27bff9ifLlVheSA4EIAABCEAAAr8SyCvgtRyuX79+KWBbbbVV6poLCEAAAhCAAARKk0BeAV+RZOsEuvvvv9/ef/99+/jjj23WrFl+mF9nyWuZnX4xEIAABCAAAQgUj0Cskl1Foh0zZowX5rfffrs1adLEK+adddZZpm1vdcSsRgMOOugg++KLLyoSPM9AAAIQgAAEIFAAgSrtwR9//PH+YJpXXnnFL6+Lin/lypV++9tjjz3WLrjgAttvv/2ivGEHAQhAAAIQgEAlCBQk4LUGXop2m222me+dr7322rbBBhuUiVaH0jRt2rSMfbqFlt4deuih/i9cgpfuzjUEIAABCEAAApUnkHeIXr3sE044wSa6U5jGjx9v3377rW2xxRb2xhtvlIk9n3DPfoD97LOJcA8BCEAAAhCoGgKxAv7nn382HTyjufNTTjnFx6g5dNndfffdsSmQgt0///lP72fYsGHWqlUr22effeyXX36JfQ5HCEAAAhCAQKkQuGPgfdamZdtSSU650hEr4KdOnep3rOvUqVNGoN3cfs4TJkzIsEu/YQ/7dBpcQwACEIBAUgns0XNPa9KoSSKTHyvgtZPdlClT7Pnnn09lbuHChXa927O5h9vqMZdhD/tcZLCHAAQgAAEIrBoCsUp2jRs3thEjRlifPn2sZcuW1sAdpTh69Gg/3B41Bx8mmT3sQxL8QgACEIAABKqHQKyAV5KOOuoovznNs88+azNnzvTa83379jUJ/1yGPexzkcEeAhCAAAQgsGoIxAp47Tuv4fl9993XTjrppFSKvv76a69RH54ul3L49ULP6ez4FStWZOxhr4Nq5s2bl+2dewhAAAIQgAAEqphApICfPXu2n3uXFv2ZZ57ph+TDeIMg8NvQfvLJJ6njY0O39OfOO+88u+eee0xz9tKo13M6uCbqufB5fiEAAQhAAAIQqBoCkQJewviQQw6xuXPn+vPge/funYpN8+va6Oayyy5L2YUXFX0ufJ5fCEAAAhCAAASqhkCkgJcA/+abb/ya9TPOOMO0r3whpqLPFRI2fiAAAQhAAAIQKJxApIAPH2/evLkX7hpq13a1CxYs8FvVHnHEEbHnwYfPheHwCwEIQAACEIDAqiUQK+CVlLPPPttGjRrld6Hr3r2735P+xhtv9Mp32ps+l9GhMq+99prXvNd1aNZbbz3bfvvtw1t+IQCBPATGf/WBrbn6WtZ+nQ55fOIMAQhA4DcCsQJeSnbaZvbTTz+1DTfc0D916aWX2l//+lcbPny43XTTTb+FlHWlbWm1Vr5jx47WsGHDlOuee+6JgE/R4AIC+Qlc99hVtuMmu9iJe5+a3zM+IAABCPxKIFbAz5gxw7SmPRTuIbUDDjjApCWfy3z11Vf27rvvmn61Bz0GAhCAAAQgAIFVSyB2q9qNN97YL2978cUXU6nSPvPa3U7D9blMo0aNTPPw66yzTi4v2EMAAhCAAAQgUEQCsQJe8Wo4fq+99jIdMKMd7KQp//LLL9vFF1+cM1nt2rWzbbfd1g/ha6kdpiyBpU5h8d1Rt9miX/nMmzbNPrjzTlu+ZElZz9hAAAIQgAAEykkgr4A/5phj/EY12slOQv6aa66xzz77zCTEcxnN3b/66qt22mmn+T3s69SpY+Hf4YcfnuuxWmO/bPFiu23/A+yx/xtoK5ct8/le5jYEevjUAXZtj5621F1jIAABCEAAApUhEDsHry1nv/vuO39yXNzpcdkJ0PC8BHyUadasWZR1rbFb4QT6Db/bxmZ8+okFK4OMfC+dP99mT55sDxx3vPW/c7TVqx9bPBnPcgMBCEAAAhBIJxArQRYtWuTn2jXcfuyxx9phhx1mq622WvrzZa61m512u9tggw3KuGFh9o1bWbBg1qwywj1kE6xYad+++aZ9/8EH1n6bbUJrfiEAAQhAAALlIhA7RN+iRQv78ccf7cgjj7RbbrnF2rZta8cff7xf3x4Vy9FHH239+vWzn376yQt5CfrsP22SU5vNVLe6YP706bEIfp461X4YNz7WD44QgAAEIACBOAKxPXg9qHPgNf+uP50id9ttt5nWsusYWQn9dPPPf/7Ta93rmcluqDnK5BsBiHqmJtktmTc/f3bcyL2U8DAQgAAEIACBihLIK+AVsJbGPf3003bXXXfZE088Yb169bLf//73ZeJMX/PeuXNnf5pc9ha3sq/NprVTVGzQtKlJqS6XaexGTtbZeKNczthDAAIQgAAE8hKIHaJf4pZsnX766bbuuuvawIEDTVvT6rhXCe099tgjNnBtcfvnP//ZfxyEW9zusssu9uGHH8Y+V9MdO++6i63ZuVNsNiXgO+ywQ6wfHCEAAQhAAAJxBGJ78Ivdci6d5z5mzBjbaaed4sLJcKvMFrcZAdXAm9XbtLG9hw61//Q9ODJ39Rs3tiPvu9earrlmpDuWEIAABCAAgUIIxPbgpWR36623lku4K9K4LW4nTpxYSLpqtJ/uBx1of37rDddL397qOEVEmbpuSVynnXe2E597xjpst22Nzj+ZgwAEIACB4hOIFfAVjb6iW9xWNL4kPtfBLT08+eWXrLHbM0BGPfuTXnzeOu64YxKzQ5qLRODmsTfZs+OetqvGXG7vffFOkWIhWAhAoCYSiB2ir0yGwy1udViNBP5LL71kq6++ur311luVCbZGPauNbNRzl6nnTtyrW69ejcofmak8gek/T7O5C3/2f/MWz6t8gIQAAQjUGgJ5BXxFz3XXFrdbbrmlvfDCC37I/sADDzStga/ty+RqzZtFRiEAAQhAoFoJ5BXwlTnXXevm9TfL7dy2xhprmA6eQcBXa3kTOQQgAAEI1BICsQK+Mue6n3XWWXbHHXdY7969rX379nbvvffasGHDbOzYsbFHzdYS7mQTAhCAAAQgUFQCsQK+oue6//LLLzZq1CibNGmStW7dOpWBiy66yK6//nq7+eabU3ZcQAACEIAABCBQ9QRitegreq77zJkzbf31188Q7kr67rvvnnML26rPGiFCAAIQgAAEai+BWAFf0XPddZKcDqYZNGiQTZkyxaSo9647ZGXw4MH+VLrai5ucQwACEIAABFYNgdgh+oqe6z5nzhz7wB13+uyzz9qQIUOsqdt7XTviyeiceJ06J/Piiy/abrvt5q/5DwIQgAAEIACBqiOQU8BX5lx37YBXyHp3TQFgIAABCEAAAhCoegKRAl497EWLFnlluLXWWisyVp37fv/990e66Qz4DTfcMNINSwhAAAIQgAAEik8gUsBzrnvxwRMDBCAAAQhAoJgEIgV89rnuxUwAYUMAAhCAAAQgUPUEYrXoqz46QoQABCBQGgRauKW8W59wnK3TtYtt3v+I0kgUqYBAFRKI7MFXVfhPPvmkPfXUU7bZZptZz549be211zYtocNAAAIQqG4Cqzn9oo332svmTPnGH9Vc3ekhfghUNYGi9eAvuOACO+GEE0znv48fP96+/fZb22KLLeyNN96o6jwQHgQgAAEIQAACWQQK6sGXtyeuDXKuvvpqv1Wtlstp7bu07rU+/u6777YddtghKxncQgACEIAABCBQlQTy9uAr0hOfOnWqdenSxTp16pSR1m7dutmECRMy7LiBAAQgAAEIQKDqCcT24CvaE99oo438FrXPP/98KsXayU4HzfTo0SNlxwUEIAABCEAAAsUhECvg03vi6TvTqSc+evTonClq3LixjRgxwvr06WMtW7a0Bg0aeP9afsccfE5sOEAAAhCAAASqjECsgK9MT/yoo47yc+3aj16ny0l7vm/fvibhj4EABCAAAQhAoLgEYgV8RXviS5cuTW1jq4NmOnToYMuXL7eHHnrIVlttNdOHQ/fu3YubM0KHAAQgAAEI1GICsQJeXCrSE1+yZIlpu1stj2vdurWtt956Nm7cOKtfv7517tzZL5k7/fTT7YorrigIvY6aldEyO4WBgQAEIAABCEAgnkCstKxoT1y99vnz59uYMWP8sHydOnVs9uzZdsABB5i08iWot99+ezvxxBNjN77RM9ttt53p2Nrp06f7nv9///tfPwoQny1cIQABCOQn0GmXXWxN1+nAQKAmEogV8BXtiX/++ee+p33wwQenmOlUumOPPdYeffRR23///a137972/vvvxwr4gQMH2t57723/+te/bPHixbbffvvZTTfdZOeee24qXC4gAAEIVJTA6m6EUX8YCNREArECvqI9cQ3Df/fdd36IfvPNN/fcVqxY4efgtcmNzpr/7LPPTAI8zrzwwgt+FEB+pA+g6YLbbrutXAJeIwl33XWXHwXIFdeRRx5pTZo0yeVsX3zxhb3yyis53eVwyCGH2BprrJHTj3byk8JhttGHi8y8eb/4UQpNaeQyGsXQCEac2XPPPa19+/Y5vWjpo3Qh4szOO+9sG2+8cU4vOkpYGxbFmW222cZvUZzLj96BUaNG5XL29tre+He/+12sn//85z+2bNmynH66du1qO+64Y053Odx3331+xCmXJ+3nsPvuu+dy9vb6cJ01a1ZOP+uuu67tu+++Od3lMHbsWPv+++8z/Iz/Ylzq/vXXX7dem+2Ruo+6ePHFF+2rr76KcvJ20oHp379/Tnc5KB7Vz1xG02R/+tOfcjln2M+YMcNuvfXWDLv0mzXXXNPSOwLpbuF1VeRJq3c+/fTTMMgyv4Xk6YMPPvBTjWUeTrM45phjrF69emk2mZcff/yxpa9IynT9393vf/97U9uby3z55Zf28ssv53L29hVtj9ID1SqoNm3apFtlXKtsH3/88Qy77Bt15KSDlcvMnTvXHnzwwVzO3n6nnXby+6rk8qQ2VG18nNl6663zLtGOe08VdiHtkVaXaeQ7l9H+MMpPnNEx7PPmzcvppWPHjrbHHvHtQPhwrICvaE+8UaNG9ve//93UyEvAq3F76aWXfEFr7v3UU0/1lUB71OcygvTDDz/4Z0M/bdu2tWnTpoW3/lfD/BI4uYxA6ZmGDRvm8uIbmDgBrwp1++2353xeDgIeJ+C15DAqjK3dy9nAPa90SoDHCXhVqKgw0hOmFyhOwKtC5QtDnOMEvCpUvjAkSOLKd+XKlXnDOProo/MKeFVs7bGQyxx00EF5BbwamOz3Kj28Xr165RXwaugmTZqU/ljG9VZbbZVXwD/99NP23nvvZTz3Y7NvzJr9z+pNbfN8SoZzmRvVM30Y5zJaqppPwL/99tv28MMP5wrCfwwXKuD10RP3rmh1TT4Br4/r5557Lmd6CsmThGpcntSByJcn6RHF5UUJ1DsbJ+A/+eSTvGHonc0n4POlQ+9sXHukD8l8YeidjRPwWh2VLwwpVOcT8PnCUJuodi2X0UhzvjDUvsftwaIOR74w1MHM1+FQx0edylxG09T5BLw6YJJ9ucyuu+5aNQK+Mj3x0047zXZx81tqcH755RcbPHiwF/ZK9F//+tcyu9xlZ0bz74LerNmvrZvzEI4opPsdOXJk+m2Z66FDh9qCBQsywsn2pA+SONOuXTvbZ5994rzEhq8H1QhFhTHvg/EWuBUGTZo0ja2QCkMVNioMuYVG8cQZCd58YUgpMs6IV74w9JUZZ6SXkS+MuI+MMGyNWKiC5zKFrNbYbbfdTCMbucwmm2ySyyllr5Gp7J0bU47uIh8P+dUHsQ5kSjev/Pi8TZv+nbfqvumm6U6R1/qgjvuYbdGiReRz6Zba5yLuozku/PRwdK341HvKZeI+aMNn1HOKU66Vjk4+o/egsnmSsMr3ztatWzc2KXoP8oVRFe3R6quvHpuOddZZJ286NLoSZ7THSb685Ctfte/5wsjXHmmflXxhSJbFmULao7iPjDBstUdx79mmBdRhCXBt657LaFSyUFPHCdEgzrPmv88666wyPXEJ7vPPP9805KS95rONhk1fe+01U49RRkP06mmp55bvS1n+taxODYmGG8PG8ZFHHjEJ7OxejvznMsOGDfM9lnyCL9fzxbYfsk5rW+B6OVL0+evkL4odHeEnjMCl915sVz401Kf68YufzTtEX13ZGzBggN14441los9lX8YjFhCAQIUISLYOGjTIy8bsAGKH6OW5oj1xDVm/8847fjhPPW/1kCS0JXALMfpiV8958uTJKQGffl1IGPiBAAQgAAEI1FYCeQW8wGjuImr+IteQpBTs1MvWPMLw4cP98LW+5DWHofndQo16+v/4xz+8csNPP/1kGo6/8sorC30cfxCAAAQgAIFaSyB+wshh0ZC6lFw08f/AAw+k/t58882c0CSMpWClORyteZdWrnrkGtLPpzmdHug555zj5+E33HBDr5hw4IEHmhRQMBCAAAQgAAEIxBPI24OX8oKWmGgePF25RsoE2qwmykgzVhrF6smr56+5eM3JS1M8XBYW9Vy2nZQ4nnnmGb9JjpRG0uPP9ss9BCAAAQhAAAK/EYgV8FJw0zax+i2Pkpo0tc844wzbcsstTUu7pPWnay3NuPzyy3+LvcArbZKDgQAEIAABCECgcAKxAl7LNbQERUsqymuGDBli2vRBRtrvd9xxh2m5kTY+wEAAAhCAAAQgUFwCsXPw0mLfdttt/faw4XK3QpKjTWqeeuopf7CM/OsjQRvcaOg+brOJQsLGDwQgAAEIQAAC+QnE9uC1tE1r3KVcp+Vy6aZfv36pI2FDe21OM2XKFL8k7swzz8wY1tdye23Bp52c8u1cFYbHLwQgAAEIQAACFSMQK+DV847axEZRpe8wF0YtIa49kNXb13Z96cPx2uFJu3RddtlloXd+IQABCEAAAhAoEoFYAS+hrH2EC92RTgL8m2++8VvTSsku396+RcoTwUIAAhCAAARqPYFYAS86FdmRTj1/rXfX+nkdSKC19KHRvsK5lteFfviFAAQgAAEIQKByBGIFfGV2pKvI+vnKZYWnIQABCEAAAhAICcQK+Owd6TTkLuU57Uin0+G0s1yUqej6+aiwsIMABCAAAQhAoPwEYpfJVXRHusqsny9/FngCAhCAAAQgAIFsArECPn1HOs2dhzvSaT/4vn37ZoeVuq/o+vlUAFxAAAIQgAAEIFApArECXiFrR7q33nrLR6Id6U466SS/nv2UU07JGXG4fl5r57WffJ06dVJ/hx9+eM7ncIAABCAAAQhAoGoIxM7BKwodEqO5+Pfff9+f566lcNKM1wlxO+64Y2Qqyrt+PjIQLCEAAQhAAAIQqDCBvAJeu86NHTvWunfvnnGamzaxySXgtX5eR7w++eSTfstaHR3bs2dPv9FNmzZtKpxYHoQABCAAAQhAoDACsQJ+6tSp9sILL5h+27ZtW1iIv/q64IIL/EY3G220ka1YscLWWGMN0xGz+ljYYYcdyhUWniEAAQhAAAIQKB+BWAGvrWfXX3/9cgt3zcFfffXV/kx4zd9ru1vtXT9nzhy7++67EfDlKyN8QwACEIAABMpNIFbJrn379n5o/oYbbvDz8IWGrh5/ly5drFOnThmPdOvWzSZMmJBhxw0EIAABCEAAAlVPIFLAH3300Smtdx3vqn3l11prrZSdtOLjtOE1LK9T5Z5//vlUihcuXGjXX3+99ejRI2XHBQQgAAEIQAACxSEQOUSv4fVBgwalYtSQu5a7yUhwd+zYMfI0ufCBxo0b24gRI6xPnz7+uQYNGtjo0aP98bFvvPFG6I1fCEAAAhCAAASKRCBSwLdu3dr0J3PzzTf7rWmnTZtmEtRnn322zZgxw58RH5emo446ys+1P/vss35ZnXbF0+Y4Ev4YCEAAAhCAAASKSyByiD6MUtrv2ujm5Zdf9sJd9mPGjLGddtrJnxYX+sv126JFC78xzoUXXmhbbbUVwj0XKOwhAAEIQAACVUwgVsB/8cUXtuaaa9qmm26airZevXqmtfHp8+spx7QL9fw1F6+NcmTOOeccf0ysTqjDQAB2P8wMAAAmf0lEQVQCEIAABCBQXAKxAl4C+ttvv/Vr18NkLF682M+vb7LJJqFVmd/K9vzLBIgFBCAAAQhAAALlIhAr4NVbHzVqlO+x6wCZXXbZxW9Y8+abb/rjYnPFVJmef64wsYcABCAAAQhAoHACsQJewRxyyCF+w5prr73WdIqcNqr5+OOPLW7L2Yr2/AtPNj4hAAEIQAACEIgjEKlFn/2ANrzRX6EmveevOXxp0L/77rumI2e1qx0GAhCAAAQgAIHiEsjbg69o9MuXL7eJEydaeXr+FY2L52oWgXPPPde+/vrrmpUpcgMBCEBgFRMoqAdf3jRNnjzZ+vfvb3Pnzo3d8a684eK/dhB45JFH/HuTvdVx7cg9uYQABCBQNQSK0oPXATU6Svb222+32bNnV01KCaVWENDGSL/88os99thjtnTp0lqRZzIJAQhAoBgEInvwOhDm+++/j41Px8duscUWkX6077y2tNUe9vrTnHxoDjvsMLv33nvDW34hkEHguuuu8zslDh061O+a2LBhwwx3biAAAQhAoDACkQL+rrvu8j0oBaHe1I8//mhNmzb1ynLSoK9bt66dd955OQV88+bN7bnnnotMweqrrx5pjyUEIAABCEAAAlVHIFLA/+Mf/zD96Tx49dKvuOIKP6eu3tRPP/1kJ598sj9dLlcy9AGg42IxEIAABCAAAQhUD4HYOfjPP//ctHPdH//4RwuHSrXs7fTTTzcpQuUzTz75pPerbWvffvttk/IdBgIQKIzAsuXLbPrP01Kev5/9na1cuTJ1zwUEIACBOAKxAr5Dhw6mveM/+OCDVBha/ibluc033zxlF3VxwQUX2AknnOCXyo0fP95veavRAI6LjaKFHQQyCXw9/Ss77O/725g37k85DLrrfDv6miNs+YrlKTsuIAABCOQiEDlEH3rW0a5XXnmlbbvttta1a1fTsqVXXnnFb3rzxBNPhN7K/Or8eJ0pP2nSJHvrrbf85jb9+vWzOXPm+J3wdthhhzLPYAEBCPyPwKxfZlnP0ze2lUFmb129+Sffe8wOdYL/3nMftiaNmoAMAhCAQE4CsT14PTVgwABTD1y/OvJVvXf1wrUULpeZOnWqn4PPXsfcrVs3k4Y+5jcCnXbZ2d+Ev7+51L6rDz/8MGMa55lnnrElS5bUOhDn33FWGeEeQli2Ypm9PekNu+eVO0MrfiEAAQhEEsgr4PVU9+7d7ZRTTrFBgwZZ3759rVmzZpGBhZbai17L5NKPlNXSueuvv9569OgReuPXEeh32yjP4bBbb6nVPC6//HJ/1oFGfUKjd06nFi5atCi0qvG/s+fNtve+eCc2n/MXz7eH3rgv1g+OEIAABGKH6EM8UpZ76qmnbLPNNrOePXva2muv7ZfMhe7ZvxraHzFihPXp08datmxpDRo0sNGjR1urVq2Yg8+Gxb3/8NOqDe18mG40paPpHu2d8NBDD5neq5puZvw83Za6Xno+M23Oj/m84A4BCNRyAnkFvJTlNCyvXrnOeV9jjTVszz339GfEx82lH3XUUSZ37Uw2c+ZM/0Gg3n9taKRr+TtVruxrp8Phw4eXEe5hIFqqKT0OfWAefPDBoXWN/W3WuJnVr5u3WlqzJuwnUWNfAjIGgSoiEDtEHyrLvf76636IXnFKWU4KdDo2Np/RYTPaGOeTTz4xzctrDT0GAukEpJOhnnqc0XsjAV8bTLu11jP9xZl6devZ7pvtEecFNwhAAAIWK+Aroyx31lln2XHHHWfTpk2zdddd129PK218CXwMBEIC8+bNM+ln5DO15UwDbRL110MvtNVjeugrVq6wk/YZkA8Z7hCAQC0nECvgK6osp+1tR40a5Xvu9913n1111VX2/vvv25/+9Cc/31rLmZP9NAKtW7e2Fi1apNlEX3bu3DnaoQba7rrZ7jbytP9Yy9VaWh33LzSNGjSyLTv/zj656Wtru+a6oTW/EIAABCIJxE72VVRZTnPuWkanxjvd7L777nbZZZelW3Fdywlo86N27drFHm601lpr+dUbtQnV/tscZK+0f9fOuuU0e27C0z7rf97vTDvzwHNs7eZr1yYU5BUCEKgggdgevI7r1Na0n376qQ0ZMsTPw48cOdK0yU3cjnQbbLCB6bQ5LavTcjltr/nuu+/a4MGD7dhjj61gUnmsJhJo1KiRaYlcmzZtIrPXpEkTvxpDxw/XNtO5zQa21YZbp7K9e889Ee4pGlxAAAL5CEQKeM13akj91VdftTPPPNMrx2mTm3322cc23HBDP/x+66235gxbSlPa3lYfBdrsRifIbbPNNj68o48+2urUcQOP7u+ll17KGQYOtYfAHnvsYY8++qjfLTE880C514eidDluu+222gODnEIAAhCoIgKRQ/RamnTIIYf4pUvz58+33r17p6KTEpDWwccNtWtOVUub8hkNzWIgIAL6ANQoj941HUwk8/DDD/u9F/wN/0EAAhCAQLkIRAp4CfBvvvnGnwV/xhln+HXwhYT6t7/9zR8rq53v1NPPZRYsWGAa6pfi1IEHHpjLG/a1jIB2SNR8e2jitkMO/fALAQhAAALRBCKH6EOvzZs398I9ff36F198ETqX+ZWWvIbl999/f7vjjju8Fr2WQekEOj33+OOPmzbO0QeAth/da6+9yoSBBQQgAAEIQAAClScQK+AVvM5y13K5Zcv+t33mOeecY9tvv70/RjY7evXatSxOQvz+++83ac3rI0GKVNriVspUOjzkzTfftPPPP59d7bIBcg8BCEAAAhCoIgKRQ/Rh2NqaVj3yl19+2e8nL/sxY8bYeeed5xXtLr744tBrxq+2qA2Pk501a5bpTx8J9erVy/DHjVldx6Td77bySofwgAAEIAABCFQVgdgevIbVtUxu0003TcUnIa09wdNPiks5RlxoPl9nySPcI+A4q4arrWZnvPsOAj4aD7YQgAAEIFBBArECXr3ub7/91h8sE4a/ePFif1KcjvHEQAACEIAABCBQmgRih+jV69aWs+qxqyevdclayrTeeuv5Ne2lmSVSlWQCWl0hHY9rrrnG628kOS+kHQIQgEB1EogV8EqY1sNPmjTJr2vX4TMDBw70O4ulb0hSnRkg7ppFQAcT6d1q3769ac8FDAQgAAEIVIxAQS3oRx995Hvs2pFODbAEPQYCxSKgTZS0AyIGAhCAAAQqTiCvgNeStxNOOMF0tvv48eP9nLwOCInbi77iyeFJCJgdfvjhfrdEWEAAAhCAQMUJxA7R//zzz3b11Venhui1N32/fv1Me83ffffdpuVwq8oo7h49ehR0tOiqShPxQAACEIAABEqVQGwPXkPxXbp0KTNc2q1bN5swYcIqy9Nzzz1nvXr18qMHqyxSIoIABCAAAQgkmECsgNcyOR33mr7mfeHChXb99df73nSx860967Vznk6g0wE4GAhAAAIQgAAECiMQO0TfuHFjv+a9T58+1rJlS7+b3ejRo61Vq1arZA5+3LhxNnPmTH/0rA6mwUAAAhCAAAQgUBiBWAGvII466ig/1/7ss896Yau18H379l0l+8jvtNNOpr848/3339vKlStzetGIAwYCEIAABCBQ2wjkFfACIg36jz/+2O8przXKOl1Oy+VKwdx6663+AJtcadE++BgIQAACEIBAbSOQV8CfddZZ/ujX3r17+81H7r33Xhs2bJjfvlbHvlaVGTBggA0fPtwHp/3rZ8yYUVDQuQ68CR9WWjEQgAAEIACB2kYgVsD/8ssvfqta7WTXunXrFJuLLrrIK9rpKNmqMhdeeKGdcsopPrj69WOTVVVREg4EIAABCECgxhKIlaRScFt//fUzhLtI6Jx37TZWlUZD/qUy7F+V+SIsCEAAAhCAQHUQiF0mJ4W6tm3b2qBBg/xyOSmz6bCZwYMH27HHHlsd6SVOCEAAAhCAAAQKIBAr4LVj3QcffGBDhgzxm91oL/ptttnG70uvtel16tTxfy+99FIBUVXOy6JFi2yzzTarXCA8DQEIQAACEKglBGKH6Fu0aOFPkcvHol27dvm84A4BCEAAAhCAwCokECvgtXtchw4d/AY32WnSNraan8dAAAIQgAAEIFB6BGKH6OfOnWs777yzff3116mUa6hcS9q0hSwGAhCAAAQgAIHSJBDbg9cQvQT8Vltt5besldLdH/7wB1tnnXVMG8xgIAABCEAAAhAoTQKxAr5evXp21VVX2d577+23p128eLFXuDv//PO9cl1pZolUQQACEIAABCAQO0QvPDpN7sorr7Q2bdrYfvvtZyNHjrQXX3wRchCAAAQgAAEIlDCBWAE/b94823TTTb2i3fjx4+2xxx4zbQ178MEHe6FfwvkiaRCAAAQgAIFaTSBWwIuMjoe95ZZbrFmzZh6UNrjRMa4NGjSo1eDIPAQgAAEIQKCUCUQKeG1De88995g2tlFvfezYsfbpp5+m8vHUU0/Ze++9l7rnAgIQgAAEIACB0iIQKeA/++wz++6771IpHTFihN+9LrRYsWKFLV++PLzlFwIQgAAEIACBEiMQKeBLLI0kBwIQgAAEIACBchJAwJcTGN4hAAEIQAACSSCAgE9CKZFGCEAAAhCAQDkJ5Nzo5ssvv7QXXnjBB6dz4TUvH95//vnn5YwG7xCAAAQgAAEIrEoCkQJeS+DuuOMO/xcm5v3337ebbropvLVDDz00dc0FBCAAAQhAAAKlRSBSwI8aNcr0h4EABCAAAQhAIJkEmINPZrmRaghAAAIQgEAsAQR8LB4cIQABCEAAAskkgIBPZrmRaghAAAIQgEAsAQR8LB4cIQABCEAAAskkgIBPZrmRaghAAAIQgEAsAQR8LB4cIQABCEAAAskkgIBPZrmRaghAAAIQgEAsAQR8LB4cIQABCEAAAskkgIBPZrmRaghAAAIQgEAsAQR8LB4cIQABCEAAAskkgIBPZrmRaghAAAIQgEAsAQR8LB4cIQABCEAAAskkgIBPZrmRaghAAAIQgEAsAQR8LB4cIQABCEAAAskkgIBPZrmRaghAAAIQgEAsAQR8LB4cIQABCEAAAskkgIBPZrmRaghAAAIQgEAsAQR8LB4cIQABCEAAAskkgIBPZrmRaghAAAIQgEAsAQR8LB4cIQABCEAAAskkgIBPZrmRaghAAAIQgEAsAQR8LB4cIQABCEAAAskkgIBPZrmRaghAAAIQgEAsAQR8LB4cIQABCEAAAskkgIBPZrmRaghAAAIQgEAsgfqxrjhCAALVSmDgQX+xj76daFtvtJ3t2HXnak0LkUMAAskiQA8+WeVFamsZgWZNmlmThk2sRdMW1rBBw1qWe7ILAQhUhgACvjL0eBYCEIAABCBQogQQ8CVaMCQLAhCAAAQgUBkCCPjK0ONZCEAAAhCAQIkSQMCXaMGQLAhAAAIQgEBlCCDgK0OPZyEAAQhAAAIlSgABX6IFQ7IgAAEIQAAClSGAgK8MPZ6FAAQgAAEIlCgBBHyJFgzJggAEIAABCFSGAAK+MvR4FgIQgAAEIFCiBBDwJVowJAsCEIAABCBQGQII+MrQ41kIQAACEIBAiRLgsJkSLRiSBYGQwO499rTObTYMb/mFAAQgUBABBHxBmPAEgeoj8Mfdj6u+yIkZAhBILAGG6BNbdCQcAhCAAAQgkJtAIgT8uHHj7LPPPsudC1wgAAEIQAACEMggUNJD9O+9957179/f2rZtawsXLrQgCOzBBx+0zp07Z2SCGwhAAAIQgAAEMgmUdA/+kksusZNOOsleffVVe//9923bbbe1a665JjMH3EEAAhCAAAQgUIZASffgTz75ZNtxxx1Tie7UqZONGTMmdc8FBCAAAQhAAALRBEpawO+///6pVC9YsMBGjhzpe/QpS3ehHv2SJUvSrTKuv/vuu4x7biAAAQhAAAK1gUBJC/iwACTcDznkEOvYsaMNHDgwtPa/ffr0sRUrVmTYpd/Q40+nwTUEIAABCNQWAiUj4AcMGGDDhw/33Ndee22bMWOGv549e7btu+++1qZNG7v//vutfv3MJHfp0iW2rJ577rlYdxwhAAEIQAACNZFAprSsxhxeeOGFdsopp/gUhEJ8+vTp1rt3b9tiiy1s1KhRZYR7NSaXqCEAAQhAAAIlTaBkBPy6665r+ks3xxxzjB+Wv+KKK2zmzJneqV69etaqVat0b1xDAAIQgAAEIJBFoGQEfFa6bOLEiTZ27Fhv3a5du5Rzhw4dbMqUKal7LiAAAQhAAAIQKEugZAV8jx49/MY2ZZOMDQQgAAEIQAAC+QiU9EY3+RKPOwQgAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QQQ8NFcsIUABCAAAQgkmgACPtHFR+IhAAEIQAAC0QRKXsAHQWCvv/66ffjhh7Zy5croXGALAQhAAAIQgEAGgfoZdyV2M3HiRDvggAOsa9eu9s0331jHjh3t4YcftiZNmpRYSkkOBCAAAQhAoLQIlHQPfuDAgTZgwAB7+umn7dNPP7WvvvrKHnjggdIiSGogAAEIQAACJUigpHvw6q03atTIY1u6dKnNnz/f6tYt6W+SEixikgQBCEAAArWRQEkL+ObNm/syGT58uI0cOdK23357O+ywwzLK6cQTT7RFixZl2KXf6IOgf//+6VZcQwACEIAABGo8gZIW8CF99eJ79Ohhr732mr355pvWq1ev0MkL/tRNxMWwYcMibLGCAAQgAAEI1GwCJTPerbl29bb116pVqwzqxx13nN1+++3Wr18/Gzx4cIYbNxCAAAQgAAEIlCVQMgL+wgsvtAkTJvi/l19+2ZYvX25nn322ff/996lUd+/e3SZNmpS65wICEIAABCAAgWgCJTNEv+6665r+0s2PP/5oQ4cOtX/96182Z84c01z8vvvum+6FawhAAAIQgAAEIgiUTA8+Im126aWX2rRp06xz5862/vrr+6H7a6+9NsordhCAAAQgAAEIpBEomR58WppSlxtssIHf2Gbu3LlWp04dC7XqUx64gAAEIAABCEAgkkBJC/gwxS1atAgv+YUABCAAAQhAoAACJT1EX0D68QIBCEAAAhCAQAQBBHwEFKwgAAEIQAACSSeAgE96CZJ+CEAAAhCAQAQBBHwEFKwgAAEIQAACSSeAgE96CZJ+CEAAAhCAQAQBBHwEFKwgAAEIQAACSSeAgE96CZJ+CEAAAhCAQAQBBHwEFKwgAAEIQAACSSeAgE96CZJ+CEAAAhCAQAQBBHwEFKwgAAEIQAACSSeAgE96CZJ+CEAAAhCAQAQBBHwEFKwgAAEIQAACSSeQiMNmKgO5bdu2dtFFF1n9+qWbVZ2Wx4E6v5UyPH5joauFCxdagwYN/F+mS+nc6bTHKNOsWTP785//HOVUEnYrVqywRYsWmdKJ+R8B6l/mm5AEHj179sxM9K93dQJnIl2wXGUE/vCHP9idd965yuIr9YjgkVlC1157re2666625ZZbZjpwV2kC33zzjY0cOdKGDh1a6bBqSgDUv8ySTDIPhugzy5I7CEAAAhCAQI0ggICvEcVIJiAAAQhAAAKZBBDwmTy4gwAEIAABCNQIAgj4GlGMZAICEIAABCCQSQABn8mDOwhAAAIQgECNIICArxHFSCYgAAEIQAACmQQQ8Jk8uIMABCAAAQjUCAKsgy+BYpw4caL16NGjBFJSGkmAR2Y5TJkyxdZcc01r3rx5pgN3lSawePFimzp1qm200UaVDqumBED9yyzJJPNAwGeWJXcQgAAEIACBGkGg3mBnakROqiETjz32mE2aNMn/qZf1yy+/WKtWraxu3cJnPiZPnmz333+/zZkzxzbYYINK5UKbEubaMrRSAbuH//vf/9paa61lTZs2zQjq2WeftSZNmtjqq6+eYR91U8z0VSRsldfzzz9fLb03bT/71FNPWfv27TO2oH3yySdtwYIF1qZNmxTCL774wr9jS5Yssa+++srWXXfdlFttvqiK+vfiiy/6d3udddaxNdZYo8I4K/L+FRrZtGnT7LXXXrMNN9ww4xG1Gap/Xbp0ybDPdVOsNFY03Oqsf19++aV9/PHH1qFDhxSuGTNm+PZA78Fqq62Wsn/ppZf89axZs5JX/1zhYCpIwJV60LVr12DrrbcOunfvHrgh1GDjjTcO3njjjYJCdHscB27oNdh7772DUaNGFfRMLk9HHnlk8N577+VyrrS92ws9cI1hmXDWW2+94N577y1jn23hBFOwzz77ZFtXyX1F8+6G3gL3wVIlaShvIMuWLfPvy9NPP5161AnywH0cBm5L2pSdLpS/M844I/j73/8eHHbYYRlutfmmsvXv4YcfDlxDHritSAO9CxU1xXy3laZHHnkkWHvttcskT+2MGKxcubKMW7bFXXfdFVxyySXZ1pW+r0zeq7P+uQ5L0LJly8CdRZBicPnll/v6p3oWmqVLl/o24tVXX01k/Su8q+m/Yfgvm8Att9xi77zzjn300Uf2008/mRNi5hph09dePqPemA66UO/42GOPzec91l29wVI2GqnQaEcxTKnnPSrPOvxot9128z2z0H3s2LF2xBFH2GeffWbqtYXm5Zdftr322svOO+88e+CBB0Jrfh2BytS/d9991w466CAbPXq0bbbZZhXmWcx3u8KJynpQ75BGhqraJCHvUXnW2Q7ioV58aNSOHHPMMX5kLbRznSZ/UNl2222XyPqHgA9Lsgp+69WrZ9dff70X2hp2D43rnfthNPfFaH369DEdcPHhhx9a3759TcOum266qamxmT9/vp188sl+eLZdu3b+hXI9PR+Mfv/2t7/5YTqdfCWlPNf7826uh2c68ejQQw/1HwthvKv69+CDD7bbb7/d3IiGPx3vwAMP9B86Gvo67rjj7LvvvjM34mE6wUsN6zXXXGOtW7e2M8880yc1ipMcypv3XOEorCuvvNIz7Ny5s7mRB1lVm9ljjz0yBLwaGL0TO+20k0nYy6gBnTlzpv8Y0KEoIat///vfdvHFF5vKXkOKeofUiNdmE1X/ctWpf/zjHzZixAh74oknbMcdd/TYVCfV8OtkRwn8xx9/PIXz22+/Nb3fmjqRwuP+++9vP/74o0W926mHVuGF0terVy+74YYbrGPHjj6drsfuU/Cf//zH7rvvPv8xdNppp9nXX39tvXv3trPOOsvXP713uTgpgPLkPS4chVUq9U9t6Lbbbpuqf2o/x40bZ0OGDLG3337bt6dKr+qUuOqDPL3+5Wrr9ExJmXAogt/yE3AFGbi5sTIPut5W4CqSt3cNiB8KcpUocA1CcOqppwabb755oKEfDXlrmOjnn38Oli9fHrivx8C9TIHr6QZuVCDYaqutgksvvdSH4xqjwM3BBa7BD9xIgR+y7dSpk3dzlcqHo/AUbjFMIUP0ytcmm2wSvPLKK4FrFAL3ERK4o3r9EKKGGd18l8+r0ucaUD+9IT7Kay5OGkIrT97jwrn77ruD9ddfP3BzasH48eM93+oaohcD13vww38arnfa3IFrdILZs2cHV199ddCvXz958VM3rqfvrzV06D7i/LWGE12jE7hGJ5g+fXowcOBAPz3kHWvJf4XUv1x1yo2cBe5jydc5TZW5+eDAfWwGgwYNCtzoW+CEop9CcSNznubuu+8euI/vwM17B26EJXDHcwZ/+ctfIt/tqsZfyBB9OL1z9NFHBxo219SP3o8JEyYErhPh86n8ul6rT7/7GPJTExqq1vuTi5PyUp68x4VTavXPqZ8FRx11lC+uBx98MNhzzz39tRP8gRsp89eaVrzpppv8dXr9y9XWeY8l9B89+CJ8bqn3HQ7Rq6elIfttttnGGjZsaBdeeKEfgnUVz59BLaU49RjUS9WXtr6q3XybV7jTOdrqjcoccMABXqFGPU8352Zuzt++//577yaFEIUjRTedG16d5pRTTrGdd97ZnCD1aVYPVGlTGqV8mH7u/QknnGD77bef7/Hn4vTBBx+UK+9x4ajHfsghh/hems5PVlqr03Tr1s00qqOeg3oKulfvUMPxzz33nLl2wtvrPspo2FAMpdj5pz/9ySsARfmrbXZh/dMSuFx1qnHjxtaoUSNfJ7X8UAp7ek8HDBjgfzXS5j6s/PPipyN7r7rqKv/+qjfndG3shx9+yPluVwdztQvqIbsPf/8O6Vf1T+2O/pTfUElWo2iXXXaZH1FU/nNxKk/e43grnFKrfxrFkPKijEbMwnqm32eeecaPNL7++uspe+8x7b+oti7NuSQu65dEKmpYIqRRL4Euo2u9JOFwuuyksauhPTXModEwmCqo6+H7RiO017Cj7FVBL7jgAnvhhRd8w+96yt4+9FfsX2nQu5GGMtFoSE5CKTTp2t8S6m5kInQq8ysN8tDEcerohhwLzXtcOJ9//rmf4w7j1FB4dZtwmF5rscMGRsPDEkBKrwS/U7CLTGY6aw05xrGODKCGWuodUP3LV6fSs69haw3TulGzdGvTx4KM3DR9oo8xaV6HH9kZnot0o/oljXN98OkjJDSqe/pgTrcr9J3Qx7Y+wmXycSo07/nCKbX6pyF66U1p6lAC/vTTT/c8VA/VuVJZq63OtbopnXW+ts4HXA3/IeCrGLoa6rfeesvc8J0PWUJcL4y+/kOjBshpn5sbJg6t/Iukm4ceeij1cSAlEFVsVUb1LLS0ymlz+gbmJbd0Q8J+VRn1WKQUmG40SiGhn14B0hsb+VWjlMuoJxSaOE5uGK3gvMeFo9EPjZxo3lpGDU51G/UiNAf6ySef2L/+9a9UctxwoY0ZM8YrAm2xxRYp+/SLdNZxnNOfqenX6fVPjbNMrjqVzkLvjfRB9I6HXKXoqIZb9VAjTerxOs17v+GQ6qOEw6owqntu6s0LolAoK14t9cpeOhemPV+65C/0G8dJSsCF5j0uHLVhpVb/1P5I50JKlhrRCDcb08iY3qP0Xn0Uz5Bf6FaKdZAh+rB0KvgrISdlGzUM6qVL0e13v/ud16ZXkPrqd0tUUhrk0oLWCzRv3ryMGKUotcsuu9g///lPL9T1wmn49dxzz/X+JEjdvI8X7nK78cYb/UupnoSMenDhtIC3qOL/9t13X7viiiv8kJYUA908u5144onmlnRlrCXNFa2mD/SxomejTByn8uQ9LhwNu6qBVk9DvR8pBFa3UQ9evXSlafvtt08lRx+FKmN9AGQ3JClPXPh3Plf9y1en0vG5pap+5YLeCTXUbl7a1zen1+KVZvXe7uaG7DWcLSVZNy/uha7CyPdup8dTkWt9eKieaUhYIw2qR/fcc485XQ2v+FdImEpjrvYhjpMEfKF5jwtHaSzV+qd6pg/q0Ejwqy2WEmY4qha6Je7XvcyYChJwhe3XoepXSmhSItN6ZSnthEbKc7JzQ+yBG2r2yjlSuJNxmvOBqxShV68cs8MOO3hlK60vd0I1cBrU3l0KdFpjr/X27ks4cBroXkErVAI66aST/BrO6667LhVeVV84/YDADUv5eKQQ5l7+wOkBpKKR4olbPZC6lyJYuG5byklKtxNW/hkp2bkeaspvHKfy5D0uHEXmhuECN6zp1xU7zf5qWwefyri7kGKiW1WQbuUVn8TqtttuS9mnK/mIbaiIJw9SstJ7WJtMIfVPCme56pSU5FRvQuN6+oHryQduI6HATR8FbloodPLKd3r39Y67D/jg/PPP9+UmD9nvduqhKrxwowle2U1Koa43HKh9UH10H/g+lqjydyM/qfooZTo3Bx+4aamUkl168uI4SfGw0LzHhaP4Sq3+uZUTvt7ceeed6TgCN5rmlRRd5yJln17/4tq61AMlcMFWta6VWBVGQ2yaywqHseLiVI9Vc+6hQky6Xw0bajhRQ17ZRkP4mruNcsv2W5l7pUGKgOlD7IWGp5EL9SZymThO5cl7XDjqlWi+Oi4dudKHfTIJxNWp7BzpPVOvOXvkRIqwqsN696NMvnc76pny2mn0Tssm0+d/Cw1D6dfzaiNymVycypv3XOEoXupfLvpVb4+Ar3qmhAgBCEAAAhCodgJlu4HVniQSAAEIQAACEIBAZQkg4CtLkOchAAEIQAACJUgAAV+ChUKSIAABCEAAApUlgICvLEGehwAEIAABCJQgAQR8CRYKSYIABCAAAQhUlgACvrIEeR4CEIAABCBQggT+H8MnP6O/elEbAAAAAElFTkSuQmCC" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_a13.pdf&quot;, 
       width = 9, height = 3.5)


## Figure A14 ----
dat_eff_share_local_unambiguous &lt;- ggpredict(lm_cand_share_local_unambiguous_base,
                                             terms = c(&quot;winner_loser_before&quot;,
                                                       &quot;incumbent&quot;))

plot_interaction(dat_eff_share_local_unambiguous)
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_a14.pdf&quot;, 
       width = 9, height = 3.5)


## Table A5 ----
models_unambiguous &lt;- list()
models_unambiguous[[&#39;M1: General elections (unambiguous)&#39;]] &lt;- lm_cand_share_general_unambiguous_base
models_unambiguous[[&#39;M2: General elections (unambiguous)&#39;]] &lt;- lm_cand_share_general_unambiguous
models_unambiguous[[&#39;M3: Local elections (unambiguous)&#39;]] &lt;- lm_cand_share_local_unambiguous_base
models_unambiguous[[&#39;M4: Local elections (unambiguous)&#39;]] &lt;- lm_cand_share_local_unambiguous


rows_add_unambiguous &lt;- rows_add_4
attr(rows_add_unambiguous, &#39;position&#39;) &lt;- c(11, 12, 13)


modelsummary(models_unambiguous,
             add_rows = rows_add_unambiguous,
             statistic = &quot;conf.int&quot;,
             fmt = &quot;%.2f&quot;,
             stars = c(&#39;*&#39; = .05, &#39;**&#39; = .01, &#39;***&#39; = .001),
             coef_omit = &quot;(party_*)|(election_*)|(county_gaa*)|(Intercept*)&quot;,
             coef_rename = rename_coefs,
             gof_omit = &quot;se_type&quot;,
             align = &quot;lll&quot;)
</code></pre>

<table class="table" style="width: auto !important; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:left;"> M1: General elections (unambiguous) </th>
   <th style="text-align:left;"> M2: General elections (unambiguous) </th>
   <th style="text-align:left;"> M3: Local elections (unambiguous) </th>
   <th style="text-align:left;"> M4: Local elections (unambiguous) </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Untreated (ref. = Defeat) </td>
   <td style="text-align:left;"> 0.50 </td>
   <td style="text-align:left;"> 0.73 </td>
   <td style="text-align:left;"> -0.16 </td>
   <td style="text-align:left;"> -0.14 </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> [-0.50, 1.50] </td>
   <td style="text-align:left;"> [-0.20, 1.67] </td>
   <td style="text-align:left;"> [-0.88, 0.56] </td>
   <td style="text-align:left;"> [-0.86, 0.58] </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Win </td>
   <td style="text-align:left;"> 0.65 </td>
   <td style="text-align:left;"> 0.91 </td>
   <td style="text-align:left;"> 0.13 </td>
   <td style="text-align:left;"> -0.43 </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> [-0.45, 1.75] </td>
   <td style="text-align:left;"> [-0.17, 1.99] </td>
   <td style="text-align:left;"> [-0.72, 0.98] </td>
   <td style="text-align:left;"> [-1.27, 0.42] </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Candidate elected in t-1 </td>
   <td style="text-align:left;"> -0.49 </td>
   <td style="text-align:left;"> -0.93 </td>
   <td style="text-align:left;"> -1.07** </td>
   <td style="text-align:left;"> -1.45*** </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> [-1.75, 0.77] </td>
   <td style="text-align:left;"> [-2.36, 0.50] </td>
   <td style="text-align:left;"> [-1.69, -0.45] </td>
   <td style="text-align:left;"> [-2.10, -0.80] </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Untreated  ×  Candidate elected in t-1 </td>
   <td style="text-align:left;"> -1.66* </td>
   <td style="text-align:left;"> -1.53* </td>
   <td style="text-align:left;"> -0.06 </td>
   <td style="text-align:left;"> 0.05 </td>
  </tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> [-3.00, -0.31] </td>
   <td style="text-align:left;"> [-2.94, -0.13] </td>
   <td style="text-align:left;"> [-0.83, 0.70] </td>
   <td style="text-align:left;"> [-0.77, 0.88] </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Win  ×  Candidate elected in t-1 </td>
   <td style="text-align:left;"> -1.77** </td>
   <td style="text-align:left;"> -1.76* </td>
   <td style="text-align:left;"> -0.19 </td>
   <td style="text-align:left;"> 0.07 </td>
  </tr>
  <tr>
   <td style="text-align:left;box-shadow: 0px 1px">  </td>
   <td style="text-align:left;box-shadow: 0px 1px"> [-3.05, -0.49] </td>
   <td style="text-align:left;box-shadow: 0px 1px"> [-3.26, -0.25] </td>
   <td style="text-align:left;box-shadow: 0px 1px"> [-1.18, 0.81] </td>
   <td style="text-align:left;box-shadow: 0px 1px"> [-0.84, 0.98] </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Election FE </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Party FE </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> County FE </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;"> ✓ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Num.Obs. </td>
   <td style="text-align:left;"> 5923 </td>
   <td style="text-align:left;"> 5923 </td>
   <td style="text-align:left;"> 8620 </td>
   <td style="text-align:left;"> 8620 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> R2 </td>
   <td style="text-align:left;"> 0.031 </td>
   <td style="text-align:left;"> 0.101 </td>
   <td style="text-align:left;"> 0.012 </td>
   <td style="text-align:left;"> 0.085 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> R2 Adj. </td>
   <td style="text-align:left;"> 0.030 </td>
   <td style="text-align:left;"> 0.090 </td>
   <td style="text-align:left;"> 0.012 </td>
   <td style="text-align:left;"> 0.078 </td>
  </tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; border:0;" colspan="100%">
<sup></sup> * p &lt; 0.05, ** p &lt; 0.01, *** p &lt; 0.001</td>
</tr>
</tfoot>
</table>

<pre><code class="r">modelsummary(models_unambiguous,
             add_rows = rows_add_unambiguous,
             statistic = &quot;conf.int&quot;,
             fmt = &quot;%.2f&quot;,
             stars = c(&#39;*&#39; = .05, &#39;**&#39; = .01, &#39;***&#39; = .001),
             coef_omit = &quot;(party_*)|(election_*)|(county_gaa*)|(Intercept*)&quot;,
             coef_rename = rename_coefs,
             gof_omit = &quot;se_type&quot;,
             align = &quot;lll&quot;,
             output = &quot;tab_a05.docx&quot;)
</code></pre>

<pre><code>## [1] &quot;tab_a05.docx&quot;
</code></pre>

<pre><code class="r">## script executed successfully on
date()
</code></pre>

<pre><code>## [1] &quot;Wed Jun 23 09:57:02 2021&quot;
</code></pre>

<pre><code class="r">sessionInfo()
</code></pre>

<pre><code>## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS  10.16
## 
## Matrix products: default
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_IE.UTF-8/en_IE.UTF-8/en_IE.UTF-8/C/en_IE.UTF-8/en_IE.UTF-8
## 
## attached base packages:
## [1] grid      stats    
## [3] graphics  grDevices
## [5] utils     datasets 
## [7] methods   base     
## 
## other attached packages:
##  [1] forcats_0.5.1     
##  [2] haven_2.3.1       
##  [3] scales_1.1.1      
##  [4] Hmisc_4.4-2       
##  [5] Formula_1.2-4     
##  [6] lattice_0.20-41   
##  [7] parameters_0.13.0 
##  [8] MuMIn_1.43.17     
##  [9] ebal_0.1-6        
## [10] jtools_2.1.2      
## [11] lubridate_1.7.10  
## [12] broom.mixed_0.2.6 
## [13] broom_0.7.4       
## [14] lme4_1.1-26       
## [15] survey_4.0        
## [16] survival_3.2-7    
## [17] Matrix_1.3-2      
## [18] WeightIt_0.11.0   
## [19] cobalt_4.2.4      
## [20] car_3.0-10        
## [21] carData_3.0-4     
## [22] cowplot_1.1.1     
## [23] here_1.0.1        
## [24] modelsummary_0.8.0
## [25] ggeffects_1.0.1   
## [26] estimatr_0.30.2   
## [27] ggplot2_3.3.3     
## [28] stringr_1.4.0     
## [29] tidyr_1.1.3       
## [30] dplyr_1.0.5       
## [31] knitr_1.31        
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.3.1       
##   [2] uuid_0.1-4         
##   [3] backports_1.2.1    
##   [4] systemfonts_1.0.1  
##   [5] plyr_1.8.6         
##   [6] TMB_1.7.19         
##   [7] splines_4.0.2      
##   [8] TH.data_1.0-10     
##   [9] digest_0.6.27      
##  [10] htmltools_0.5.1.1  
##  [11] fansi_0.4.2        
##  [12] magrittr_2.0.1     
##  [13] checkmate_2.0.0    
##  [14] cluster_2.1.0      
##  [15] openxlsx_4.2.3     
##  [16] officer_0.3.16     
##  [17] sandwich_3.0-0     
##  [18] jpeg_0.1-8.1       
##  [19] colorspace_2.0-0   
##  [20] rvest_0.3.6        
##  [21] ggrepel_0.9.1      
##  [22] mitools_2.4        
##  [23] xfun_0.20          
##  [24] crayon_1.4.1       
##  [25] zoo_1.8-8          
##  [26] glue_1.4.2         
##  [27] kableExtra_1.3.1   
##  [28] gtable_0.3.0       
##  [29] emmeans_1.5.4      
##  [30] webshot_0.5.2      
##  [31] abind_1.4-5        
##  [32] annotater_0.1.3    
##  [33] mvtnorm_1.1-1      
##  [34] DBI_1.1.1          
##  [35] Rcpp_1.0.6         
##  [36] viridisLite_0.4.0  
##  [37] xtable_1.8-4       
##  [38] htmlTable_2.1.0    
##  [39] foreign_0.8-81     
##  [40] stats4_4.0.2       
##  [41] htmlwidgets_1.5.3  
##  [42] httr_1.4.2         
##  [43] RColorBrewer_1.1-2 
##  [44] ellipsis_0.3.2     
##  [45] pkgconfig_2.0.3    
##  [46] farver_2.1.0       
##  [47] nnet_7.3-15        
##  [48] utf8_1.2.1         
##  [49] tidyselect_1.1.1   
##  [50] labeling_0.4.2     
##  [51] rlang_0.4.11       
##  [52] reshape2_1.4.4     
##  [53] munsell_0.5.0      
##  [54] cellranger_1.1.0   
##  [55] tools_4.0.2        
##  [56] cli_2.5.0          
##  [57] generics_0.1.0     
##  [58] sjlabelled_1.1.7   
##  [59] evaluate_0.14      
##  [60] tables_0.9.6       
##  [61] zip_2.1.1          
##  [62] pander_0.6.3       
##  [63] purrr_0.3.4        
##  [64] nlme_3.1-152       
##  [65] mime_0.10          
##  [66] xml2_1.3.2         
##  [67] compiler_4.0.2     
##  [68] rstudioapi_0.13    
##  [69] curl_4.3           
##  [70] png_0.1-7          
##  [71] tibble_3.1.1       
##  [72] statmod_1.4.35     
##  [73] stringi_1.5.3      
##  [74] highr_0.8          
##  [75] gdtools_0.2.3      
##  [76] texreg_1.37.5      
##  [77] nloptr_1.2.2.2     
##  [78] markdown_1.1       
##  [79] vctrs_0.3.8        
##  [80] pillar_1.6.0       
##  [81] lifecycle_1.0.0    
##  [82] estimability_1.3   
##  [83] data.table_1.14.0  
##  [84] insight_0.14.0     
##  [85] flextable_0.6.3    
##  [86] R6_2.5.0           
##  [87] latticeExtra_0.6-29
##  [88] gridExtra_2.3      
##  [89] rio_0.5.16         
##  [90] codetools_0.2-18   
##  [91] boot_1.3-27        
##  [92] MASS_7.3-53        
##  [93] assertthat_0.2.1   
##  [94] rprojroot_2.0.2    
##  [95] withr_2.4.2        
##  [96] multcomp_1.4-16    
##  [97] mgcv_1.8-33        
##  [98] bayestestR_0.8.2   
##  [99] hms_1.0.0          
## [100] rpart_4.1-15       
## [101] coda_0.19-4        
## [102] minqa_1.2.4        
## [103] rmarkdown_2.6      
## [104] snakecase_0.11.0   
## [105] base64enc_0.1-3
</code></pre>

</body>

</html>
